From ef60c3985991a40e8c64a5422a1845777f466ce2 Mon Sep 17 00:00:00 2001 From: Gibeom Date: Sun, 23 Aug 2026 00:35:50 +0900 Subject: [PATCH 1/7] =?UTF-8?q?Feat(#102):=20=ED=95=9C=EA=B5=AD=EC=96=B4?= =?UTF-8?q?=20=EC=9D=B8=EC=BD=94=EB=8D=94=EB=A5=BC=20=ED=8C=8C=EC=9D=B8?= =?UTF-8?q?=ED=8A=9C=EB=8B=9D=ED=95=98=EB=8A=94=20base=20model=20=EC=B6=94?= =?UTF-8?q?=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 기존 KoreanEncoderPhishingClassifier는 인코더를 완전히 얼린 채 (torch.no_grad + mean pooling) LogisticRegression만 얹는 구조라, 정상과 피싱의 표면이 거의 같은 "기관 안내문" 구간을 가르지 못했다. real_holdout 실측에서 정상택배 78.6%, 정상공공기관 71.0% 오탐인 반면 어휘가 겹치지 않는 일상대화는 2.0%로, 어휘가 아니라 표현 자체가 병목임이 드러났다. 인코더까지 분류 헤드와 함께 end-to-end로 학습시킨다. 기존 BasePhishingClassifier 계약을 그대로 지켜 stacking 쪽은 손대지 않는다. frozen 버전과 다른 두 가지: - 파인튜닝 가중치는 __getstate__에서 반드시 보존한다. 사전학습 가중치는 다시 내려받으면 그만이지만 파인튜닝 결과는 모델 자체라 빠지면 복원할 방법이 없다 (artifact가 66MB 늘어난다). - 학습 디바이스는 CPU로 고정했다. MPS/CUDA는 같은 시드로도 커널 구현에 따라 결과가 흔들려 다른 머신에서 재현되지 않는데, 학습 규모가 작아(train 692건, 16.5M 파라미터) CPU로도 수 분이라 속도보다 재현성을 택했다. 추론은 frozen 버전과 같은 이유로 한 건씩 처리한다 - 묶어서 패딩하면 배치 구성에 따라 결과가 미세하게 흔들리는데, 평가는 묶어서 하고 운영은 단건으로 하는 구조라 그 차이가 그대로 재현성 문제가 된다. 아직 stacking factory에 등록하지 않았다 - 하이퍼파라미터를 validation에서 선정한 뒤 별도 커밋으로 교체한다. --- .../modeling/korean_encoder_finetuned.py | 357 ++++++++++++++++++ .../modeling/test_korean_encoder_finetuned.py | 269 +++++++++++++ 2 files changed, 626 insertions(+) create mode 100644 data_science/SMSModel/modeling/korean_encoder_finetuned.py create mode 100644 tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py diff --git a/data_science/SMSModel/modeling/korean_encoder_finetuned.py b/data_science/SMSModel/modeling/korean_encoder_finetuned.py new file mode 100644 index 0000000..cfdf022 --- /dev/null +++ b/data_science/SMSModel/modeling/korean_encoder_finetuned.py @@ -0,0 +1,357 @@ +"""사전학습 한국어 인코더를 파인튜닝하는 피싱 분류기 + +기존 KoreanEncoderPhishingClassifier는 인코더를 완전히 얼린 채 +mean pooling 임베딩 위에 LogisticRegression만 얹는다. 범용 임베딩이라 +"기관 안내문"처럼 정상과 피싱의 표면이 거의 같은 구간을 가르지 못했다 +(#102 진단: real_holdout에서 정상택배 78.6%, 정상공공기관 71.0% 오탐인 반면 +어휘가 겹치지 않는 일상대화는 2.0%). 인코더까지 태스크에 맞춰 학습시켜 +그 구간의 표현을 직접 다듬는다. +""" +from __future__ import annotations + +from typing import Any, Self + +import numpy as np +import pandas as pd + +from data_science.SMSModel.modeling.base import ( + BasePhishingClassifier, + ScoreOutput, + ScoreType, +) + +DEFAULT_MODEL_ID = "beomi/KcELECTRA-small-v2022" + +# frozen 버전과 동일한 값 - 벤치마크에서 p95 23.85ms, 절단 2.0%로 확인했다 +DEFAULT_MAX_LENGTH = 256 + +DEFAULT_EPOCHS = 4 +DEFAULT_BATCH_SIZE = 16 +DEFAULT_LEARNING_RATE = 2e-5 +DEFAULT_WEIGHT_DECAY = 0.01 +DEFAULT_RANDOM_STATE = 42 + +# MPS는 같은 시드로도 커널 구현에 따라 결과가 흔들려 다른 머신에서 재현되지 +# 않는다. 학습 규모가 작아(train 692건, 16.5M 파라미터) CPU로도 전체 학습이 +# 3분 이내라, 속도보다 재현성을 택한다. +DEFAULT_DEVICE = "cpu" + +# 라벨을 정수로 고정해 fold마다 클래스 순서가 뒤집히지 않게 한다 +LABEL_TO_INDEX: dict[str, int] = {"normal": 0, "phishing": 1} + + +class FineTunedKoreanEncoderClassifier(BasePhishingClassifier): + """사전학습 인코더를 분류 헤드까지 end-to-end로 파인튜닝하는 분류기""" + + def __init__( + self, + *, + model_id: str = DEFAULT_MODEL_ID, + max_length: int = DEFAULT_MAX_LENGTH, + epochs: int = DEFAULT_EPOCHS, + batch_size: int = DEFAULT_BATCH_SIZE, + learning_rate: float = DEFAULT_LEARNING_RATE, + weight_decay: float = DEFAULT_WEIGHT_DECAY, + random_state: int = DEFAULT_RANDOM_STATE, + device: str = DEFAULT_DEVICE, + ) -> None: + if epochs < 1: + raise ValueError("epochs must be at least 1") + + if batch_size < 1: + raise ValueError("batch_size must be at least 1") + + self.model_id = model_id + self.max_length = max_length + self.epochs = epochs + self.batch_size = batch_size + self.learning_rate = learning_rate + self.weight_decay = weight_decay + self.random_state = random_state + self.device = device + + # 파인튜닝된 가중치는 artifact에 함께 저장해야 하므로 상태로 보관한다 + # (frozen 버전은 사전학습 가중치를 그대로 다시 받아오면 그만이었다) + self._weights: dict[str, Any] | None = None + self._model = None + self._tokenizer = None + self._is_fitted = False + + @property + def model_name(self) -> str: + """평가 보고서와 artifact에서 사용할 모델 이름""" + return "korean_encoder_kcelectra_finetuned" + + @property + def score_type(self) -> ScoreType: + """분류 헤드의 softmax 확률을 반환한다""" + return ScoreType.PROBABILITY + + @staticmethod + def _validate_dataframe( + df: pd.DataFrame, + *, + require_label: bool, + ) -> None: + """학습 또는 추론에 필요한 DataFrame 구조와 값을 검증""" + if not isinstance(df, pd.DataFrame): + raise TypeError("input must be a pandas DataFrame") + + required_columns = {"text_norm"} + + if require_label: + required_columns.add("label") + + missing = required_columns - set(df.columns) + + if missing: + raise ValueError( + f"missing fine-tuned Korean encoder columns: {missing}" + ) + + if df.empty: + raise ValueError( + "fine-tuned Korean encoder input DataFrame is empty" + ) + + if df[list(required_columns)].isna().any().any(): + raise ValueError( + "fine-tuned Korean encoder input contains missing values" + ) + + if not df["text_norm"].map( + lambda value: isinstance(value, str) + ).all(): + raise TypeError("text_norm values must be strings") + + if require_label: + labels = set(df["label"].astype(str)) + supported_labels = set(LABEL_TO_INDEX) + + if not labels.issubset(supported_labels): + raise ValueError( + f"unsupported labels: {labels - supported_labels}" + ) + + if labels != supported_labels: + raise ValueError( + "training data must contain both normal and phishing labels" + ) + + def _seed_everything(self) -> None: + """같은 random_state면 같은 가중치가 나오도록 난수원을 고정""" + import random + + import torch + + random.seed(self.random_state) + np.random.seed(self.random_state) + torch.manual_seed(self.random_state) + + def _load_tokenizer(self): + """토크나이저를 지연 로드. 파인튜닝해도 어휘는 그대로다""" + if self._tokenizer is None: + from transformers import AutoTokenizer + + self._tokenizer = AutoTokenizer.from_pretrained(self.model_id) + + return self._tokenizer + + def _build_model(self): + """사전학습 가중치 위에 새 분류 헤드를 얹은 모델을 생성""" + from transformers import AutoModelForSequenceClassification + + return AutoModelForSequenceClassification.from_pretrained( + self.model_id, + num_labels=len(LABEL_TO_INDEX), + ) + + def _restored_model(self): + """파인튜닝된 가중치를 적용한 추론용 모델을 지연 복원""" + if self._model is None: + if self._weights is None: + raise RuntimeError( + "fine-tuned Korean encoder weights are unavailable" + ) + + model = self._build_model() + model.load_state_dict(self._weights) + model.to(self.device) + model.eval() + self._model = model + + return self._model + + def _class_weights(self, indices: np.ndarray): + """정상이 피싱의 2배인 불균형을 손실에서 보정 (balanced와 같은 정의)""" + import torch + + counts = np.bincount(indices, minlength=len(LABEL_TO_INDEX)) + + if (counts == 0).any(): + raise ValueError( + "training data must contain both normal and phishing labels" + ) + + weights = len(indices) / (len(LABEL_TO_INDEX) * counts) + return torch.tensor(weights, dtype=torch.float32, device=self.device) + + def fit(self, train_df: pd.DataFrame) -> Self: + """전달된 train split만 사용해 인코더까지 파인튜닝""" + import torch + from torch.nn import CrossEntropyLoss + + self._validate_dataframe(train_df, require_label=True) + self._seed_everything() + + tokenizer = self._load_tokenizer() + model = self._build_model() + model.to(self.device) + model.train() + + texts = train_df["text_norm"].astype(str).tolist() + indices = ( + train_df["label"] + .astype(str) + .map(LABEL_TO_INDEX) + .to_numpy(dtype=np.int64) + ) + + loss_function = CrossEntropyLoss( + weight=self._class_weights(indices) + ) + optimizer = torch.optim.AdamW( + model.parameters(), + lr=self.learning_rate, + weight_decay=self.weight_decay, + ) + + # 셔플 순서까지 시드에 묶어 fold 재실행 시 같은 결과가 나오게 한다 + generator = np.random.default_rng(self.random_state) + row_count = len(texts) + + for _ in range(self.epochs): + order = generator.permutation(row_count) + + for start in range(0, row_count, self.batch_size): + batch = order[start : start + self.batch_size] + + encoded = tokenizer( + [texts[index] for index in batch], + truncation=True, + max_length=self.max_length, + padding=True, + return_tensors="pt", + ).to(self.device) + + targets = torch.tensor( + indices[batch], + dtype=torch.long, + device=self.device, + ) + + optimizer.zero_grad() + logits = model(**encoded).logits + loss_function(logits, targets).backward() + optimizer.step() + + model.eval() + + # artifact에 담을 수 있도록 CPU 텐서로 내려 보관 + self._weights = { + name: tensor.detach().to("cpu").clone() + for name, tensor in model.state_dict().items() + } + self._model = model + self._is_fitted = True + return self + + def _require_fitted(self) -> None: + """학습되지 않은 분류기의 사용을 차단""" + if not self._is_fitted: + raise RuntimeError("fine-tuned Korean encoder model is not fitted") + + if self._weights is None: + raise RuntimeError( + "fine-tuned Korean encoder weights are unavailable" + ) + + def predict_scores(self, df: pd.DataFrame) -> ScoreOutput: + """각 메시지의 phishing 확률을 반환 + + frozen 버전과 같은 이유로 한 건씩 처리한다. 묶어서 패딩하면 같은 + 문자라도 배치 구성에 따라 결과가 미세하게 흔들리는데, 평가는 묶어서 + 하고 운영은 단건으로 하는 구조라 그 차이가 재현성 문제가 된다. + """ + import torch + + self._require_fitted() + self._validate_dataframe(df, require_label=False) + + tokenizer = self._load_tokenizer() + model = self._restored_model() + phishing_index = LABEL_TO_INDEX["phishing"] + + probabilities: list[float] = [] + + for value in df["text_norm"]: + encoded = tokenizer( + str(value), + truncation=True, + max_length=self.max_length, + return_tensors="pt", + ).to(self.device) + + with torch.no_grad(): + logits = model(**encoded).logits + + scores = torch.softmax(logits, dim=-1)[0] + probabilities.append(float(scores[phishing_index])) + + return ScoreOutput( + values=np.asarray(probabilities, dtype=float), + score_type=self.score_type, + ) + + def __getstate__(self) -> dict[str, Any]: + """파인튜닝된 가중치는 artifact에 반드시 포함한다 + + frozen 버전은 사전학습 가중치를 다시 내려받으면 됐지만, 파인튜닝된 + 가중치는 이 모델 자체라 빠지면 복원할 방법이 없다. 재구성 가능한 + 모델 객체와 토크나이저만 제외한다. + """ + state = self.__dict__.copy() + state["_model"] = None + state["_tokenizer"] = None + return state + + def __setstate__(self, state: dict[str, Any]) -> None: + """역직렬화 후 모델은 최초 사용 시 가중치로부터 다시 복원""" + self.__dict__.update(state) + + def get_metadata(self) -> dict[str, Any]: + """모델 재현과 artifact 추적에 필요한 설정을 반환""" + return { + **super().get_metadata(), + "preprocessing": { + "input_column": "text_norm", + "normalization": ( + "app.analysis.text.preprocessing.normalize_text" + ), + }, + "encoder": { + "model_id": self.model_id, + "max_length": self.max_length, + "pooling": "classification_head", + "weights_frozen": False, + }, + "fine_tuning": { + "epochs": self.epochs, + "batch_size": self.batch_size, + "learning_rate": self.learning_rate, + "weight_decay": self.weight_decay, + "class_weight": "balanced", + "random_state": self.random_state, + "device": self.device, + }, + } diff --git a/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py b/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py new file mode 100644 index 0000000..4292ab9 --- /dev/null +++ b/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py @@ -0,0 +1,269 @@ +"""파인튜닝 한국어 인코더 분류기 테스트 + +실제 인코더를 내려받거나 학습시키지 않도록 모델과 토크나이저를 결정적 +스텁으로 대체한다. 확인하는 것은 표현 품질이 아니라 Adapter 계약과 +직렬화 규약이다 - 특히 frozen 버전과 정반대로 "가중치가 artifact에 +반드시 실려야 한다"는 점이다. +""" + +from __future__ import annotations + +from pathlib import Path + +import joblib +import numpy as np +import pandas as pd +import pytest +import torch + +from data_science.SMSModel.modeling.base import ScoreType +from data_science.SMSModel.modeling.korean_encoder_finetuned import ( + DEFAULT_MAX_LENGTH, + DEFAULT_MODEL_ID, + LABEL_TO_INDEX, + FineTunedKoreanEncoderClassifier, +) + +PHISHING_MARKERS = ("급등", "종목", "입장", "인증번호") + +# 스텁 토크나이저가 만드는 입력 차원 +_FEATURE_SIZE = len(PHISHING_MARKERS) + 1 + + +class _StubTokenizer: + """마커 포함 여부를 그대로 특징 벡터로 만드는 결정적 토크나이저""" + + def __call__( + self, + text, + *, + truncation=True, + max_length=None, + padding=False, + return_tensors="pt", + ): + texts = [text] if isinstance(text, str) else list(text) + rows = [] + + for value in texts: + hits = [float(marker in str(value)) for marker in PHISHING_MARKERS] + rows.append(hits + [float(len(str(value)) % 7) / 7.0]) + + return _StubEncoding( + {"features": torch.tensor(rows, dtype=torch.float32)} + ) + + +class _StubEncoding(dict): + """transformers의 BatchEncoding처럼 .to()를 지원하는 최소 구현""" + + def to(self, device): + return _StubEncoding( + {key: value.to(device) for key, value in self.items()} + ) + + +class _StubOutput: + def __init__(self, logits: torch.Tensor) -> None: + self.logits = logits + + +class _StubModel(torch.nn.Module): + """실제 인코더 대신 학습 가능한 선형 계층 하나만 둔 모델""" + + def __init__(self) -> None: + super().__init__() + self.linear = torch.nn.Linear(_FEATURE_SIZE, len(LABEL_TO_INDEX)) + + def forward(self, *, features: torch.Tensor) -> _StubOutput: + return _StubOutput(self.linear(features)) + + +@pytest.fixture +def classifier( + monkeypatch: pytest.MonkeyPatch, +) -> FineTunedKoreanEncoderClassifier: + """인코더를 내려받지 않는 분류기""" + monkeypatch.setattr( + FineTunedKoreanEncoderClassifier, + "_build_model", + lambda self: _StubModel(), + ) + monkeypatch.setattr( + FineTunedKoreanEncoderClassifier, + "_load_tokenizer", + lambda self: _StubTokenizer(), + ) + + # 스텁은 선형 계층 하나라, 실제 인코더용 학습률(2e-5)로는 몇 스텝 안에 + # 아무것도 학습되지 않는다. 스텁 규모에 맞춰 키운다. + return FineTunedKoreanEncoderClassifier( + epochs=30, + learning_rate=0.1, + ) + + +@pytest.fixture +def training_dataframe() -> pd.DataFrame: + """양쪽 label이 모두 들어간 균형 학습 데이터""" + rows: list[dict[str, str]] = [] + + for index in range(20): + rows.append( + { + "text_norm": f"오늘 가족 모임 시간을 안내합니다 {index}", + "label": "normal", + } + ) + rows.append( + { + "text_norm": f"급등 종목 무료 공개 오픈채팅 입장 {index}", + "label": "phishing", + } + ) + + return pd.DataFrame(rows) + + +@pytest.fixture +def scored() -> pd.DataFrame: + return pd.DataFrame( + { + "text_norm": [ + "급등 종목 무료 공개 오픈채팅 입장 코드", + "오늘 가족 모임 시간을 안내합니다", + ] + } + ) + + +def test_scores_are_phishing_probabilities( + classifier: FineTunedKoreanEncoderClassifier, + training_dataframe: pd.DataFrame, + scored: pd.DataFrame, +) -> None: + """확률 방향이 phishing 쪽이어야 meta-classifier가 부호를 오해하지 않는다""" + classifier.fit(training_dataframe) + + output = classifier.predict_scores(scored) + + assert output.score_type == ScoreType.PROBABILITY + assert ((output.values >= 0.0) & (output.values <= 1.0)).all() + assert output.values[0] > output.values[1] + + +def test_fine_tuned_weights_are_serialized( + classifier: FineTunedKoreanEncoderClassifier, + training_dataframe: pd.DataFrame, + scored: pd.DataFrame, + tmp_path: Path, +) -> None: + """frozen 버전과 정반대로, 파인튜닝된 가중치가 빠지면 복원할 방법이 없다. + + 사전학습 가중치는 다시 내려받으면 그만이지만 파인튜닝 결과는 이 모델 + 자체다. artifact 왕복 후에도 같은 확률이 나와야 한다. + """ + classifier.fit(training_dataframe) + expected = classifier.predict_scores(scored).values + + artifact_path = tmp_path / "encoder.joblib" + joblib.dump(classifier, artifact_path) + restored = joblib.load(artifact_path) + + assert restored._weights is not None + assert restored._model is None + assert restored._tokenizer is None + + np.testing.assert_array_equal( + restored.predict_scores(scored).values, + expected, + ) + + +def test_training_is_reproducible_for_the_same_seed( + monkeypatch: pytest.MonkeyPatch, + training_dataframe: pd.DataFrame, + scored: pd.DataFrame, +) -> None: + """같은 random_state면 같은 확률이 나와야 artifact를 재현할 수 있다""" + monkeypatch.setattr( + FineTunedKoreanEncoderClassifier, + "_build_model", + lambda self: _StubModel(), + ) + monkeypatch.setattr( + FineTunedKoreanEncoderClassifier, + "_load_tokenizer", + lambda self: _StubTokenizer(), + ) + + first = FineTunedKoreanEncoderClassifier( + epochs=30, learning_rate=0.1, random_state=7 + ) + second = FineTunedKoreanEncoderClassifier( + epochs=30, learning_rate=0.1, random_state=7 + ) + + first.fit(training_dataframe) + second.fit(training_dataframe) + + np.testing.assert_array_equal( + first.predict_scores(scored).values, + second.predict_scores(scored).values, + ) + + +def test_predict_before_fit_is_rejected( + classifier: FineTunedKoreanEncoderClassifier, +) -> None: + """학습 전 추론은 조용히 통과하면 안 된다""" + with pytest.raises(RuntimeError): + classifier.predict_scores(pd.DataFrame({"text_norm": ["안녕하세요"]})) + + +def test_training_requires_both_labels( + classifier: FineTunedKoreanEncoderClassifier, +) -> None: + """한쪽 label만으로 학습하면 확률이 상수가 된다""" + single_label = pd.DataFrame( + { + "text_norm": ["급등 종목 무료 공개", "오픈채팅 입장 코드"], + "label": ["phishing", "phishing"], + } + ) + + with pytest.raises(ValueError): + classifier.fit(single_label) + + +def test_missing_text_norm_is_rejected( + classifier: FineTunedKoreanEncoderClassifier, +) -> None: + """다른 base model과 같은 입력 컬럼을 요구해야 한다""" + with pytest.raises(ValueError): + classifier.fit( + pd.DataFrame({"text": ["급등 종목"], "label": ["phishing"]}) + ) + + +def test_metadata_records_the_fine_tuning_settings( + classifier: FineTunedKoreanEncoderClassifier, +) -> None: + """재현에 필요한 값은 artifact metadata에 남아야 한다""" + metadata = classifier.get_metadata() + + assert metadata["model_name"] == "korean_encoder_kcelectra_finetuned" + assert metadata["encoder"]["model_id"] == DEFAULT_MODEL_ID + assert metadata["encoder"]["max_length"] == DEFAULT_MAX_LENGTH + assert metadata["encoder"]["weights_frozen"] is False + assert metadata["fine_tuning"]["epochs"] == 30 + assert metadata["fine_tuning"]["class_weight"] == "balanced" + + +def test_cpu_is_the_default_device() -> None: + """MPS/CUDA는 커널 구현에 따라 결과가 흔들려 다른 머신에서 재현되지 않는다. + + 학습 규모가 작아 CPU로도 전체 학습이 수 분이라, 속도보다 재현성을 택한 + 결정을 회귀 테스트로 고정한다. + """ + assert FineTunedKoreanEncoderClassifier().device == "cpu" From 5f5ed20c6449ca9350136f8a64ca0374c01b884e Mon Sep 17 00:00:00 2001 From: Gibeom Date: Sun, 23 Aug 2026 01:13:18 +0900 Subject: [PATCH 2/7] =?UTF-8?q?Feat(#102):=20stacking=20base=20model?= =?UTF-8?q?=EC=9D=84=20=ED=8C=8C=EC=9D=B8=ED=8A=9C=EB=8B=9D=20=EC=9D=B8?= =?UTF-8?q?=EC=BD=94=EB=8D=94=EB=A1=9C=20=EA=B5=90=EC=B2=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit frozen 임베딩 대신 FineTunedKoreanEncoderClassifier를 등록하고 기본 epoch을 validation 기준으로 10에 고정했다. ## epoch 선정 (판정셋 미사용) validation(141건)에서 4/6/8/10을 시드 3개(42/7/123)로 비교했다. real_holdout은 채택 게이트의 판정셋이라 선정에 쓰지 않았다. | 구성 | ROC-AUC | 정상 평균 확률 | 정상 오탐 | |---|---:|---:|---:| | frozen(기존) | 0.9237 | 0.254 | 13.0% | | 파인튜닝 6ep | 0.9556 | 0.151 | 6.1% | | 파인튜닝 10ep | 0.9656 | 0.053 | 1.8% | (파인튜닝 수치는 시드 3개 평균) 10 epoch이 모든 시드·모든 지표에서 우세했다. 8 epoch에서 한 번 지표가 내려앉아 처음엔 과학습으로 판단했으나, 시드를 바꾸면 재현되지 않는 단발성 변동이었다 - validation 정상이 92건뿐이라 run 간 변동이 epoch 효과보다 클 수 있어 시드 3개로 확인했다. ## 남은 확인 stacking 전체 재학습과 채택 게이트(uncertain_normal_share) 측정은 별도로 진행한다. 이 커밋은 base model 교체까지만 담는다. --- .../modeling/korean_encoder_finetuned.py | 7 +++++- data_science/SMSModel/modeling/stacking.py | 12 ++++++---- .../SMSModel/modeling/test_korean_encoder.py | 12 ++++++---- .../modeling/test_korean_encoder_finetuned.py | 24 +++++++++++++++++++ 4 files changed, 46 insertions(+), 9 deletions(-) diff --git a/data_science/SMSModel/modeling/korean_encoder_finetuned.py b/data_science/SMSModel/modeling/korean_encoder_finetuned.py index cfdf022..9dc1aa8 100644 --- a/data_science/SMSModel/modeling/korean_encoder_finetuned.py +++ b/data_science/SMSModel/modeling/korean_encoder_finetuned.py @@ -25,7 +25,12 @@ # frozen 버전과 동일한 값 - 벤치마크에서 p95 23.85ms, 절단 2.0%로 확인했다 DEFAULT_MAX_LENGTH = 256 -DEFAULT_EPOCHS = 4 +# validation(141건)에서 4/6/8/10 epoch을 시드 3개(42/7/123)로 비교해 골랐다. +# 10 epoch이 모든 시드·모든 지표에서 우세했다 - 정상 평균 확률 0.151→0.053, +# 정상 오탐 6.1%→1.8%(6 epoch 대비). 8 epoch에서 한 번 지표가 내려앉지만 +# 시드를 바꾸면 재현되지 않는 단발성 변동이라 과학습 신호가 아니다. +# 판정셋(real_holdout)은 선정에 쓰지 않았다 - 채택 게이트 오염 방지. +DEFAULT_EPOCHS = 10 DEFAULT_BATCH_SIZE = 16 DEFAULT_LEARNING_RATE = 2e-5 DEFAULT_WEIGHT_DECAY = 0.01 diff --git a/data_science/SMSModel/modeling/stacking.py b/data_science/SMSModel/modeling/stacking.py index 7d9de35..aae771c 100644 --- a/data_science/SMSModel/modeling/stacking.py +++ b/data_science/SMSModel/modeling/stacking.py @@ -21,8 +21,8 @@ ScoreOutput, ScoreType, ) -from data_science.SMSModel.modeling.korean_encoder import ( - KoreanEncoderPhishingClassifier, +from data_science.SMSModel.modeling.korean_encoder_finetuned import ( + FineTunedKoreanEncoderClassifier, ) from data_science.SMSModel.modeling.linear_svm import ( LinearSvmPhishingClassifier, @@ -78,8 +78,12 @@ def _default_base_model_factories( "naive_bayes": _build_text_only_naive_bayes, "logistic_regression": LogisticRegressionPhishingClassifier, "linear_svm": LinearSvmPhishingClassifier, - # 학습 표본이 유형당 2~6건인 구간을 사전학습 표현으로 보완 (#98) - "korean_encoder": KoreanEncoderPhishingClassifier, + # 학습 표본이 유형당 2~6건인 구간을 사전학습 표현으로 보완 (#98). + # frozen 임베딩(KoreanEncoderPhishingClassifier)에서 파인튜닝으로 + # 교체했다 - 얼린 범용 표현으로는 정상과 피싱의 표면이 거의 같은 + # "기관 안내문" 구간을 가르지 못했다. validation 기준 정상 오탐 + # 13.0%→1.8%, ROC-AUC 0.924→0.966 (시드 3개 평균, #102). + "korean_encoder": FineTunedKoreanEncoderClassifier, } def normalize_base_scores(output: ScoreOutput) -> np.ndarray: diff --git a/tests/data_science/SMSModel/modeling/test_korean_encoder.py b/tests/data_science/SMSModel/modeling/test_korean_encoder.py index 862b9ff..058fedb 100644 --- a/tests/data_science/SMSModel/modeling/test_korean_encoder.py +++ b/tests/data_science/SMSModel/modeling/test_korean_encoder.py @@ -146,12 +146,16 @@ def test_encoder_weights_are_not_serialized( assert restored._is_fitted -def test_registered_as_a_stacking_base_model() -> None: - """factory에 등록되지 않으면 학습에 전혀 참여하지 않는다""" +def test_no_longer_registered_as_a_stacking_base_model() -> None: + """stacking은 파인튜닝 버전을 쓴다 (#102). + + 얼린 범용 임베딩으로는 정상과 피싱의 표면이 거의 같은 "기관 안내문" + 구간을 가르지 못해 FineTunedKoreanEncoderClassifier로 교체했다. 이 + 클래스 자체는 비교 기준선으로 남겨두되, 실제 학습에는 참여하지 않는다. + """ factories = _default_base_model_factories() - assert "korean_encoder" in factories - assert isinstance( + assert not isinstance( factories["korean_encoder"](), KoreanEncoderPhishingClassifier, ) diff --git a/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py b/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py index 4292ab9..3c7f2ef 100644 --- a/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py +++ b/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py @@ -23,6 +23,9 @@ LABEL_TO_INDEX, FineTunedKoreanEncoderClassifier, ) +from data_science.SMSModel.modeling.stacking import ( + _default_base_model_factories, +) PHISHING_MARKERS = ("급등", "종목", "입장", "인증번호") @@ -260,6 +263,27 @@ def test_metadata_records_the_fine_tuning_settings( assert metadata["fine_tuning"]["class_weight"] == "balanced" +def test_registered_as_a_stacking_base_model() -> None: + """factory에 등록되지 않으면 학습에 전혀 참여하지 않는다""" + factories = _default_base_model_factories() + + assert "korean_encoder" in factories + assert isinstance( + factories["korean_encoder"](), + FineTunedKoreanEncoderClassifier, + ) + + +def test_default_epochs_matches_the_validation_selection() -> None: + """epoch은 validation에서 시드 3개로 골랐다 (#102). + + 4/6/8/10 비교에서 10이 모든 시드·모든 지표에서 우세했다. 8에서 한 번 + 지표가 내려앉지만 시드를 바꾸면 재현되지 않는 단발성 변동이라, 그 + 하락을 과학습으로 오해해 6으로 되돌리지 않도록 값을 고정한다. + """ + assert FineTunedKoreanEncoderClassifier().epochs == 10 + + def test_cpu_is_the_default_device() -> None: """MPS/CUDA는 커널 구현에 따라 결과가 흔들려 다른 머신에서 재현되지 않는다. From 34f2c3513d6ea83e2c329ed0ff4f92156824d9d5 Mon Sep 17 00:00:00 2001 From: Gibeom Date: Sun, 23 Aug 2026 03:42:06 +0900 Subject: [PATCH 3/7] =?UTF-8?q?Fix(#102):=20=ED=94=BC=EC=8B=B1=20=ED=95=99?= =?UTF-8?q?=EC=8A=B5=20=EB=8D=B0=EC=9D=B4=ED=84=B0=EC=9D=98=20=EA=B8=B0?= =?UTF-8?q?=EA=B4=80=EB=AA=85=20=EA=B5=90=EC=B2=B4=20=EC=95=84=ED=8B=B0?= =?UTF-8?q?=ED=8C=A9=ED=8A=B8=2022=EA=B1=B4=20=EC=A0=9C=EA=B1=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 파인튜닝 인코더로 바꾼 뒤 채택 게이트의 uncertain_normal_share가 오히려 나빠져(0.4342 -> 0.6513) 원인을 추적한 결과, 모델이 아니라 임계값 선정이 무너져 있었다. ## 원인 경계 선정은 train pool OOF에서 coverage_recall 0.95를 맞추는 지점으로 normal_max를 정한다. 피싱 231건 기준 11건까지 놓쳐도 되는데, 그 예산 전부를 고유 문자 3종의 복제본이 먹고 있었다. 하위 11건을 열어보니 1144자짜리 같은 본문에서 기관명 2~5글자만 바뀐 것들이었다: ... 신한은행 ... (원본) ... 우리카드 ... (유사도 0.9965) ... KB국민 ... ... 새마을금고 ... (총 9벌) #107이 정상 데이터에서 제거한 "기관명 교체 저다양성 중복"과 같은 오염이 피싱 쪽에 남아 있었다. 전체 스캔 결과 27개 그룹이 나왔고, 이번 커밋은 진단된 원인에만 대응해 학습 pool 피싱 22건만 지운다 (그룹당 대표 1건은 유지하므로 정보 손실 없음). 정상 22건과 평가셋 22건은 별도 안건으로 남긴다 - 변인을 하나로 제한해야 효과를 귀인할 수 있고, 판정셋을 건드리면 이전 측정과 비교가 불가능해진다. ## 효과 (판정셋 real_holdout 510건 불변, 직접 비교 가능) | 지표 | 제거 전 | 제거 후 | |---|---:|---:| | 선정된 normal_max | 0.0655 | 0.1638 | | uncertain_normal_share | 0.5822 | 0.1908 | | OOF 오탐 상한 | 0.5553 | 0.1367 | | test_accuracy | 0.6667 | 0.9111 | 기준(0.133)에는 아직 못 미쳐 게이트는 FAIL이지만, 남은 격차는 데이터 위생이 아니라 모델의 실제 분리력 문제로 좁혀졌다 - 경계를 어떻게 조정해도 통과 조합이 없고(recall이 normal_max 0.20에서 0.9277로 무너진다), OOF 하위 꼬리도 중복이 아니라 채용부업사기· 중고거래사기 같은 진짜 어려운 사례로 채워져 있다. ## 그 외 - n_splits 5 -> 10: fold 학습량 부족이 OOF 편향의 원인이라 보고 올렸으나 효과는 미미했다(normal_max 0.0597 -> 0.0655). 진짜 원인은 위의 중복이었다. 되돌리지 않고 유지한다 - fold 모델이 최종 모델에 가까워지는 것 자체는 맞는 방향이고, 손해가 없다. - 66MB로 불어난 stacking artifact는 .gitignore에 추가했다. 학습이 CPU + 고정 시드로 결정적이라 재생성 가능하고, metadata.json의 model_sha256으로 검증할 수 있다. --- .gitignore | 8 + .../phishing_total_dataset_reclassified.csv | 24 +- .../stacking/v9-experiment/metadata.json | 38 +- .../stacking/v9-experiment/model.joblib | Bin 2627276 -> 0 bytes .../reports/adoption_gate_v9_finetuned.json | 113 +++ .../adoption_gate_v9_finetuned_10fold.json | 113 +++ .../adoption_gate_v9_finetuned_dedup.json | 113 +++ .../reports/dataset_split_summary.json | 382 ++++---- .../SMSModel/reports/dataset_split_summary.md | 170 ++-- .../reports/error_analysis_v9_finetuned.json | 775 ++++++++++++++++ .../test_evaluation.json | 235 ++--- .../stacking_v9-experiment/test_evaluation.md | 26 +- .../SMSModel/run_stacking_training.py | 27 +- data_science/SMSModel/splits/sms_split_v6.csv | 854 +++++++++--------- 14 files changed, 1962 insertions(+), 916 deletions(-) delete mode 100644 data_science/SMSModel/artifacts/stacking/v9-experiment/model.joblib create mode 100644 data_science/SMSModel/reports/adoption_gate_v9_finetuned.json create mode 100644 data_science/SMSModel/reports/adoption_gate_v9_finetuned_10fold.json create mode 100644 data_science/SMSModel/reports/adoption_gate_v9_finetuned_dedup.json create mode 100644 data_science/SMSModel/reports/error_analysis_v9_finetuned.json diff --git a/.gitignore b/.gitignore index aeb227d..489f2e6 100644 --- a/.gitignore +++ b/.gitignore @@ -22,3 +22,11 @@ scripts/benchmark/output/ # A fresh clone must run --collect with AWS access before --offline can succeed. data_science/SMSModel/artifacts/stacking/llm_*_predictions.json data_science/SMSModel/artifacts/stacking/*.json.tmp + +# 파인튜닝 인코더가 들어간 stacking artifact (약 66MB). +# 얼린 인코더를 쓰던 시절에는 2.5MB라 커밋했지만, 파인튜닝 가중치는 +# 모델 자체라 artifact에 포함해야 하고 재학습마다 히스토리에 영구 +# 누적된다(레포 .git 전체가 21MB). 학습을 CPU + 고정 시드로 +# 결정적으로 만들어 뒀으므로 run_stacking_training.py로 정확히 +# 재생성할 수 있고, metadata.json의 model_sha256으로 검증한다. +data_science/SMSModel/artifacts/stacking/v9-experiment/model.joblib diff --git a/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv b/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv index 50b6ad2..3cd22cc 100644 --- a/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv +++ b/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv @@ -1,4 +1,4 @@ -text,label,type,has_url,source +text,label,type,has_url,source [Web발신] [건강지키미] 12월 13일 휴면회원전환예정 https://www.baemin.me/h5ivDR,normal,기타정상,True,original [Web발신] [신한은행] 2차 소비쿠폰 11/29 19:28 쌍용스토아신부점 사용 107만 원 잔여 740원,normal,정상카드결제알림,False,real_holdout "[Web발신] [2차(민사소송)검토대상 통보] 000님 1,589원 /우리은행 (계좌번호) - 갤럭시아",phishing,금융기관사칭,False,original @@ -305,7 +305,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[한진택배] 상품 배송 안내 기쁨을 전하는 행복상자 한진택배입니다. 고객님의 소중한 상품을 가지고 출발합니다. ■ 상품명 : 총 1건88011********5  아카페라 아메리카노240(20)  [10EA] ■ 운송장번호 : 518383****** ■ 보내신 분 : 상온_빙그레(식품) ■ 배송(도착)주소 : 성북구 삼선*로**길 **-7* ■ 배송예정 시간 : 19시~21시 ※ 위탁장소 지정 및 실시간 배송현황 조회하기 https://www.nhis.or.kr/rOiZTP ※ 택배 상자는 운송장, 포장재 등을 제거 후 분리배출해 주시기 바랍니다.",normal,정상택배배송안내,True,original [Web발신] [ 일양로지스 배송안내 ] 안녕하세요? 일양로지스입니다. 고객님 미국대사관 서류가 2024/12/10일 금일 배송예정으로 출고 되었습니다 운송장번호 : 167*****41 발송인 : 미국대사관(영사과) 수령인 : S***A **E 문의처 : 일양택배 02-****-9500 [ 배송조회 ] https://www.hansung.ac.kr/w3B9eB 감사합니다.,normal,정상택배배송안내,True,original 그럼 삼양도 제일 인기 많은 삼양라면만 가격을 인하한거야?,normal,일상대화,False,real_holdout -[국외발신]000님 [NH농협은행] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original [Web발신] 안녕하세요? [365 토마토의원] (혜화동 13**번지)입니다. 병원 주차 불편 해소를 위해 병원 지하 주차장 만차 시 도보 3분 거리에 전용 주차장을 마련하였습니다. 내원시 편하게 이용하시기 바랍니다. - 일시 : 2026. 4. 1일 이후 - 위치 : 서북구 혜와동 **59번지 [F2 주차장] - H.P. 010 **** **31 - * 네이버맵 링크 이용하세요 https://www.kakaobank.com/xpAKQT,normal,기타정상,True,real_holdout "[Web발신] 기회를 놓치지 마세요, 시간이 다시 오지 않습니다! 다음 급등주를 잡고 싶으신가요? 우리 주식 채팅방에 빨리 가입하세요! 전문가 팀이 실시간으로 시장동향과 종목 선택 기술을 공유하여 투자에 큰 도움을 드립니다! 한정된 시간 동안 활동 보상: 쇼핑 쿠폰, 현금, 전자 제품 등, 선착순으로 제공됩니다! 기회를 놓치지 마세요, 지금 바로 가입히세요! 텔레그램 링크 https://appsiz.cfd/U8PFK",phishing,금융기관사칭,True,real_holdout 사랑하는 모친께서 별세하셨기에 삼가알려드립니다 장례식장 https://reurl.cc/VscFO,phishing,경조사사칭,True,original @@ -351,7 +350,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] 긴급생활비 지원사업이 접수되었습니다 다시한번 확인 부탁드립니다 https://gourl.kr/0tmMz,phishing,정부공공기관사칭,True,original "거의 그 정도 하더라ㅋㅋ. 내가 말한 샌드커피 논탄토도 카이막, 토스트빵 세트가 7,500원임!",normal,일상대화,False,original 나이키 [Web발신] (광고)[나이키] 인기 제품 추가 할인! 얼마 남지 않은 최대 40% 할인의 시즌 마감 세일. 새로 추가된 인기 제품을 놓치지 마세요. ▶ 세일 제품 구매하기 https://www.hansung.ac.kr/dH0Khr 무료수신거부 080-810-****,normal,정상광고프로모션,True,real_holdout -Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니다(reurl.cc/RLZGyg) 방금 수신한 문자메시지는 해외에서 발송되었습니다.,phishing,금융기관사칭,False,original "이랜드이츠 우*정*대 입학설명회 안내 [Web발신] 안녕하세요 이랜드이츠 채용팀입니다. 이랜드이츠 산학협력 연계로 2025년 '대전 우*정*대 애슐리학과(창업조리제빵과)'를 운영합니다. 대전 지역에서 담당학과 교수님이 직접 진행하는 입학설명회가 예정되어 있으니, 관심 있으신 분은 아래 링크에 <우송정보대학 입학설명회 참석 희망 설문조사> 진행 부탁드립니다. (~10/30 수요일까지) https://kindly-bonsai-7e1.notion.site/12afe441cdef80a5a844c9a022bccac2 감사합니다.",normal,기타정상,True,real_holdout [Web발신] 국내 우한폐렴 급속도확산 감염자및 접촉자 신분정보 확인하기 https://appsiz.cfd/taQyM,phishing,정부공공기관사칭,True,original [Web발신] 마감 임 박 입니다. 구엉 테크에 들어가서 벌써 165-255 처를 결과냈습니다. 현재는 라이브 강의중. 즉시 저희 방에 놀러오세요. 만일 문제가 있을경우 모든 손 실 을 저희가 모든 첵임지겠습니다. 아래 링크 누르시고 즉시 밴드에 입장하시면 내용 전달 드리겠습니다. https://n.bnuee.cyou/TiYtI,phishing,투자리딩방사기,True,original @@ -412,7 +410,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 "[국외결제안내]금액:1,807원 결제완료 인증번호:1154 배송조회거래 환불 취소문의 전화:02-6736-**** 방금 수신한 문자메시지는 해외에서 발송되었습니ㅏㄷ.",phishing,금융기관사칭,False,original [국세청][환급내역안내] 종합소득세 환급대상자입니다. 금액을 확인하시고 수령하세요. (https://Oa.SK/XFVCU,phishing,정부공공기관사칭,True,original 그리고 그릭요거트 처럼 질감이 꾸덕해서 빵이나 크래커랑 같이 먹으면 잘 어울려ㅋㅋ.,normal,일상대화,False,original -[국외발신]000님 [카카오뱅크] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original 즐거운 설명절 보내세요.(^0^)//선착순 무료 상품권을 드립니다. 선물 받기 (https://t.ly/1ii1x,phishing,이벤트당첨사칭,True,original [Web발신] 000님 중요한 건으로 문자드립니다 확인해주세요 https://n.bnuee.cyou/y4EDH,phishing,악성링크앱설치유도,True,original "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.gov.kr/0fahjD",normal,정상공공기관알림,True,original @@ -533,7 +530,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 [Web발신] [2차(민사소송)검토대상 통보] 000님 602원 /신한은행 (계좌번호) - 갤럭시아,phishing,금융기관사칭,False,original [Web발신] 국내 우한폐렴 급속도확산 감염자및 접촉자 신분정보 확인하기 https://Oa.SK/ixQgk,phishing,정부공공기관사칭,True,original "[Web발신] 대기번호 2번 고객님 지금 입장해 주세요. ㅡ 10분 동안 미 입장시 대기 접수가 자동 취소되며, 다시 대기 등록을 해주셔야 합니다. 대기 취소하기: https://www.toss.im/euhWFd",normal,기타정상,True,original -[국외발신]000님 [신한은행] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original [국제발신] 농협BC고객님 11/30 해외쇼핑140원 결 제완료 본인사용이 아니신경우 콜센터로 접수바랍니다. (1899-7862),phishing,금융기관사칭,False,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.kakaobank.com/V9hnjM,normal,기타정상,True,original "[Web발신] [U폰캐시 소멸예정] 안*영 고객님, 고객님께서 보유하신 50,000C 중 소멸예정일까지 미사용 시 50,000C이(가) 소멸될 예정입니다. ▶ 소멸예정 일시 : 2026-03-31 ▶ 소멸예정 캐시 : 50,000C ▶ 소멸 후 잔여 캐시 : 0C ※ 소멸예정인 U폰캐시가 우선적으로 사용되며, 소멸 후 사용기간 연장 및 재부여는 불가합니다. ※ U폰캐시 상세 내역은 유모바일 App 및 홈페이지를 통해 확인하실 수 있습니다. ※ 이 메시지는 고객님이 보유하신 U폰캐시의 소멸 예정 사실을 사전에 안내하는 메시지입니다. ※ 이 메시지는 계약 또는 거래관계에 따라 지급된 U폰캐시 소멸 안내 메시지입니다. > 내 U폰캐시 확인하기 https://www.uplusumobile.com/my/ucash/history/S 감사합니다.",normal,정상포인트소멸알림,True,original @@ -627,7 +623,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 "[Web발신] 00님께, [CU]에서 전달된 소식입니다! ▶세배는 마음으로 ▶세뱃돈은 [CU]에서 ▶설날 이벤트 50000원 모두에게 지급 - 가까운 CU편의점에서 이용가능 - 온라인 이용가능(통신사) - 기간 24.02.01 ~ 24.02.05 홈페이지: https://t.ly/iuz4i",phishing,이벤트당첨사칭,True,original [Web발신] ★미납안내★ 가상 계좌 카드 가능 (https://n.bnuee.cyou/DV9fM,phishing,금융기관사칭,True,real_holdout 신년 연하장이 도착하였습니다. 2024년 새해 복 많이 받으세요. (https://div.bnuy.my/Ogew9,phishing,이벤트당첨사칭,True,real_holdout -[국외발신]000님 [롯데카드] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original "(광고)[배달의민족] [Web발신] (광고)[배달의민족] 오늘 10시 [대한민국vs멕시코] [맥도날드 선착순 112만 원 쿠폰] 포함 [총 108,500원 할인] 혜택이 곧 사라져요 지금 확인하기 https://www.naver.me/mhgcZo 무료수신거부 : 0808880219",normal,정상광고프로모션,True,original "[Web발신] [TOPCIT] 05.16(토) 9시10분까지 배정 고사실 도착바랍니다. ※ 신분증, 수험표 필수 지참 및 응시자유의사항 필독, 9시 10분 이후 고사실 입실 불가",normal,기타정상,False,real_holdout 삼가알려드립니다.부친께서 금일 별세 하셨습니다.장례식장https://Oa.SK/MFiPd,phishing,경조사사칭,True,original @@ -818,7 +813,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 "[롯데월드]종합이용권 받아가세요~ [Web발신] (광고)나의 롯데월드 MBTI 확인하고 롯데월드 가자! 이*현님에게 딱 맞는 롯데월드 코스를 추천해주는 나만의 월드 MBTI를 찾아보세요♬ 월드 MBTI를 확인만 해도, 롯데월드 종합이용권과 모바일 상품권을 드립니다♡ ▼월드 MBTI 확인하러 가기▼ bit.ly/3ZxqnT4 - 월드 MBTI 선택 약관에 동의한 손님에 한해 이벤트에 자동 응모됩니다. 당첨 확률을 높이고 싶다면? 월드 MBTI를 #공유해 주세요! ▶이벤트 자세히 보기 bit.ly/4gfhEud - 본 문자는 롯데월드 어드벤처를 방문하신 손님분들 중 SMS 마케팅 수신에 동의하신 분들에 한해 발송되었습니다. - 광고성 정보 수신 동의 여부 변경을 원하실 경우 [롯데월드 어드벤처 홈페이지>로그인>마이페이지>회원정보수정]에서 변경 가능하며, 동의 철회 시 광고, 이벤트 공지 등의 정보를 수신하실 수 없습니다.",normal,정상광고프로모션,False,real_holdout [Web발신] 90대어르신도가능 공공기관도인정 유명인다수 20분으로평생행복하게사세요 https://gourl.kr/snzRA,phishing,악성링크앱설치유도,True,original 키링을 가방에 달고 다니는 연예인 있음ㅋㅋ? 이런 유행에 연예인이 안 했을리 없자나.,normal,일상대화,False,original -[국외발신]000님 [하나은행] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original "[국외발신] 결제내역 금액 1,862원 코드번호 48** 승인완료 되었습니다 - 본인구매 아닐경우 050-5173-5275",phishing,금융기관사칭,False,original 아빠! 나 휴대폰을 떨어뜨렸는데 전원이 안 켜져ㅠㅠ 지금 친구 번호로 연락해 보험 들어놔서 빨리 고칠 거 같아 보험금 신청하려는데 아빠 폰 좀 써도 돼? 아빠 계좌번호랑 비밀번호도 좀 알려줘! https://div.bnuy.my/0qcUu 이거 링크 눌러서 앱 다운 받으면 내가 알아서 할게 다운만 해줘~,phishing,지인가족사칭,True,real_holdout "(광고)[배달의민족] [Web발신] (광고)[배달의민족] 오늘 10시 [대한민국vs멕시코] [맥도날드 선착순 74,532원 쿠폰] 포함 [총 108,500원 할인] 혜택이 곧 사라져요 지금 확인하기 https://www.naver.me/xJ9Pj0 무료수신거부 : 0808880219",normal,정상광고프로모션,True,original @@ -898,7 +892,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 신년 연하장이 도착하였습니다. 2024년 새해 복 많이 받으세요. (https://t.ly/NaLTk,phishing,이벤트당첨사칭,True,real_holdout "[Web발신][네이버pay] 삼성 지펠 냉장고 상품번호:1684317 1,445원 결 제완료 문의:02-830-0614",phishing,금융기관사칭,False,original [Web발신][네이버pay] 삼성 지펠 냉장고 상품번호:1684317 435원 결 제완료 문의:02-830-0614,phishing,금융기관사칭,False,original -"23년 새정부 설 민생안정대책 긴급생계비대출 지원공고 [Web발신] (광고)대상자 알림 [2023년 새정부 설 연휴 서민경제 안정 대책 저소득·취약계층 근로자 및 위기가구 주거안정·생계비 부담 지원 방안 ""긴급 생계비대출"" 신청 안내] 안녕하세요 귀하께서는 지속되는 서민경제 위기 속에 겨울철 난방비, 가스비 등 공공요금 인상과 설 명절을 앞두고 오르는 밥상 물가 상승으로 생계부담을 겪고 있는 저소득·취약계층 근로자와 위기가구의 주거안정 및 생계 부담을 낮추기 위한 정부 민생안정대책 방안으로 시행되는 【긴급 생계비대출】 지원 대상자로 확인되어 안내드립니다. ※전화상담을 통해 비대면으로 신청 가능하며, 조기 마감이 예상되오니 아래의 지원내용을 확인하신 후 신청하시기 바랍니다. ■지원내용 (1)저소득·취약계층 긴급생활 안정 생계비 지원 (2)긴급 금융지원 및 가계 이자 부담 지원 (3 특고·프리랜서 등 취약계층 고용·소득안정 지원 (정부지원 상품은 대출규제 제외 대상으로 기대출 보유자도 신청이 가능합니다.) ■융자조건 -주관기관: 기획재정부 -지원예산: 2조 5천억 원 규모 -보증기관: 신용보증재단, 보증비율 100%(전액보증) -취급기관: 우리카드 -대출금리: 연 2.64% ~ 3.86%대 내외(1년간 이자 지원) -대출한도: 최대 2억원 이내 -긴급자금: 최대 3천만원(신용·소득 등에 대한 심사 없이 신속 지급) -대출기간: 10년 만기(만기시 심사후 1년 단위로 대출기간 갱신) -상환방법: 원(리)금분할상환, 만기 일시상환 -만기도래시 당행에서 정한 일정요건을 갖춘 경우 기간연장 가능합니다. ※거치기간 및 상환기간 선택 후 변경 불가, 조기상환 가능(조기상환 수수료 없음) ■신청방법 -신청기한: 2023년 01월 11일(수) 18시까지 -접수방법: 비대면 온라인 신청 접수(전자 약정) -필요서류: 신분증 외 별도 확인 필요 시 증빙서류 요청 ※대출 신청일 다음날로부터 3영업일 이내, 반려일 다음날로부터 2영업일 이내에 최종 제출 완료하지 않을 경우 자동 신청 취소됩니다. ■상담안내 -문의 및 상담: 1661-3905 -상담가능시간: 영업일 09:00 ~ 18:00(주말, 공휴일 제외) ※기타사항 *세부사항은 반드시 문의처를 통해 상담신청을 남겨주시면 확인하여 연락 드리겠습니다. 무료수신거부 080.887.4746",phishing,금융기관사칭,False,original [알바몬] 제트알바 도착 [Web발신] [알바몬] 딱 맞는 제트알바가 도착했어요! 000님께 딱 맞는 알바가 도착했어요! 제트 지원으로 더 빠르고 간편하게 지원해 보세요. - 공고제목: [분양상담사] 작년 월급 몇배로 받고 싶다면? 지원… - 기업명: 주식회사 일월그룹 - 업직종 : 마케팅·광고·홍보 외 2개 - 근무기간 : 3개월~6개월 - 근무지 : 경기 광주시 - 급여: 월급 500만원 ▶채용정보 확인하 https://www.hansung.ac.kr/sgaZQD ▶추천알바 모두보기 https://www.baemin.me/rqX4AD - 해당 메시지는 회원님이 요청하신 조건에 맞는 채용공고가 있을 경우 발송됩니다. - [알바몬앱 > 마이페이지 > 알림설정]에서 제트알바 알림을 해제할 수 있습니다.,normal,기타정상,True,original "[Web발신] mini카드 유효기간 만료 자동 해지 안내 [우리은행] 김*아님, mini카드(4335)의 유효기간이 2025년 10월 만료되어 자동으로 해지되었습니다. ※ 유의사항 - 선불 티머니 잔액 환불 받으려면?: https://www.gov.kr/EK3CDN - 반드시 카드 실물이 있어야 티머니 잔액 환불이 가능하며, 카드를 폐기하시면 티머니 정책에 따라 잔액 환불이 불가합니다. - 티머니 잔액 환불 및 이전, 선불충전금 보호조치 등 상세 내용은 티머니 고객센터(1644-0088)로 문의하여 주시기 바랍니다.",normal,정상금융알림,True,original 사랑하는 모친께서 별세하셨기에 삼가알려드립니다 장례식장 https://div.bnuy.my/AoRfX,phishing,경조사사칭,True,original @@ -1145,7 +1138,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 [Web발신] 90대어르신도가능 공공기관도인정 유명인다수 20분으로평생행복하게사세요 https://div.bnuy.my/ms5eG,phishing,악성링크앱설치유도,True,original [국세청][환급내역안내] 종합소득세 환급대상자입니다. 금액을 확인하시고 수령하세요. (https://appsiz.cfd/XDFAR,phishing,정부공공기관사칭,True,original 안녕하세요 쿠팡입니다. [Web발신] 쿠팡 이용해주시는 고객님 한정 쿠팡 제휴사체험단 지원이 가능합니다! 제휴사체험단의 제품을 무상으로 체험하시고 체험수당(현금)도 받아가세요! ☞ 하루 최대 30~100만 체험수당(현금)과 체험단제품을 무상으로 체험하세요! 지금바로 확인하시고 체험! 을 외쳐주세요! 이번 체험단은 무더운 여름을 타파할 혜택이 가득합니다. 상담 채널: https://div.bnuy.my/Jrrm5 -체험! 이라고 말씀하시면 됩니다. 아래 혜택 꼼꼼하게 확인부탁드립니다! 혜택1. 여름을 시원하게 타파할 체험단 체험수당(현금)! 혜택2. 마련된 체험상품들 모두 무료증정! 혜택3. 선정된 인원들가운데 호캉스 숙박권 지급!,phishing,채용부업사기,True,real_holdout -"23년 새정부 설 민생안정대책 긴급생계비대출 지원공고 [Web발신] (광고)대상자 알림 [2023년 새정부 설 연휴 서민경제 안정 대책 저소득·취약계층 근로자 및 위기가구 주거안정·생계비 부담 지원 방안 ""긴급 생계비대출"" 신청 안내] 안녕하세요 귀하께서는 지속되는 서민경제 위기 속에 겨울철 난방비, 가스비 등 공공요금 인상과 설 명절을 앞두고 오르는 밥상 물가 상승으로 생계부담을 겪고 있는 저소득·취약계층 근로자와 위기가구의 주거안정 및 생계 부담을 낮추기 위한 정부 민생안정대책 방안으로 시행되는 【긴급 생계비대출】 지원 대상자로 확인되어 안내드립니다. ※전화상담을 통해 비대면으로 신청 가능하며, 조기 마감이 예상되오니 아래의 지원내용을 확인하신 후 신청하시기 바랍니다. ■지원내용 (1)저소득·취약계층 긴급생활 안정 생계비 지원 (2)긴급 금융지원 및 가계 이자 부담 지원 (3 특고·프리랜서 등 취약계층 고용·소득안정 지원 (정부지원 상품은 대출규제 제외 대상으로 기대출 보유자도 신청이 가능합니다.) ■융자조건 -주관기관: 기획재정부 -지원예산: 2조 5천억 원 규모 -보증기관: 신용보증재단, 보증비율 100%(전액보증) -취급기관: 신한카드 -대출금리: 연 2.64% ~ 3.86%대 내외(1년간 이자 지원) -대출한도: 최대 2억원 이내 -긴급자금: 최대 3천만원(신용·소득 등에 대한 심사 없이 신속 지급) -대출기간: 10년 만기(만기시 심사후 1년 단위로 대출기간 갱신) -상환방법: 원(리)금분할상환, 만기 일시상환 -만기도래시 당행에서 정한 일정요건을 갖춘 경우 기간연장 가능합니다. ※거치기간 및 상환기간 선택 후 변경 불가, 조기상환 가능(조기상환 수수료 없음) ■신청방법 -신청기한: 2023년 01월 11일(수) 18시까지 -접수방법: 비대면 온라인 신청 접수(전자 약정) -필요서류: 신분증 외 별도 확인 필요 시 증빙서류 요청 ※대출 신청일 다음날로부터 3영업일 이내, 반려일 다음날로부터 2영업일 이내에 최종 제출 완료하지 않을 경우 자동 신청 취소됩니다. ■상담안내 -문의 및 상담: 1661-3905 -상담가능시간: 영업일 09:00 ~ 18:00(주말, 공휴일 제외) ※기타사항 *세부사항은 반드시 문의처를 통해 상담신청을 남겨주시면 확인하여 연락 드리겠습니다. 무료수신거부 080.887.4746",phishing,금융기관사칭,False,original "[Web발신] (광고) 2024 부메랑 1만원 할인 쿠폰이 아직 남아 있어요! 아직 ★ 2024 부메랑 1만원 할인 쿠폰 ★이 고객님의 쿠폰함에 있다는 사실, 알고 계셨나요? 부메랑 회원님만 받을 수 있는 이 특별한 혜택을 2024년이 가기 전에 가까운 아웃백 매장에서 꼭! 사용해 보세요! 아웃백은 언제나 고객님을 기다리고 있습니다♥ 곧 만나길 바라요 ────────────── 쿠폰 확인: bit.ly/obkreminder ※ 아웃백 앱에서 발급된 쿠폰을 확인한 후, 매장 방문 시 직원에게 쿠폰을 보여주세요. ────────────── ★ 본 문자는 11월 18일 0시 기준 '2024 부메랑 1만원 할인 쿠폰'을 아웃백 앱에 소지하고 계신 회원님에 한하여 발송되었습니다 ★ - 본 문자는 11월 17일 기준 수신 동의 회원에게 발송되었습니다. ☎ ****-0500 ■ 무료수신거부: ***-810-0**7",normal,정상광고프로모션,False,real_holdout [국제발신] 안녕하세요! ETC 사용 요금 미납 건이 확인되었습니다. 기한 내 납부 부탁드립니다. (https://appsiz.cfd/smggI,phishing,금융기관사칭,True,real_holdout (광고)[배달의민족] 미확인 보상 안내 [Web발신] (광고)[배달의민족] 고객님은 [1만원 쿠폰] 수령 대상자입니다. 소멸 전 보상 확인 부탁드립니다. - 주문시 즉시 적용 가능 1만원 쿠폰 확인▼ https://www.go.kakaobank.io/Hj4PWW 무료수신거부 : 0808880219,normal,정상광고프로모션,True,real_holdout @@ -1174,7 +1166,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 **[열린옷장]** [Web발신] 강*웅 님의 의류가 정상적으로 반납되었습니다. 감사합니다. 열린옷장 택배대여 다시 대여할 때는 택배대여를 이용하면 더 편리합니다. https://www.kakaobank.com/LqJP7L 열린옷장 인스타그램 열린옷장에서 진행하는 무료 프로그램과 취준 정보를 볼 수 있습니다. https://www.hansung.ac.kr/Zuvz5M,normal,정상택배배송안내,True,original [Web발신][처리] 귀하의 경범되 위반통지 1건 - 3회 미납시 형사처벌 안내: https://n.bnuee.cyou/iptnX,phishing,정부공공기관사칭,True,original [Web발신] 안녕하세요 여행000입니다. 전쟁 이슈로 인해 유류세가 크게 할증되고 있습니다. 부득이하게 오늘 발급을 빠르게 진행하기 위해 중도금을 입금해주세요. 농협 010-0000-000-00 여행 000 URL: https://appsiz.cfd/ozcbK,phishing,금융기관사칭,True,real_holdout -[국외발신]000님 [KB국민은행] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original [국외발신] KRW 259원 결 제 완료 문의센터 000-0000-0000,phishing,금융기관사칭,False,original "[Web발신] 대기번호 2번 고객님 지금 입장해 주세요. ㅡ 10분 동안 미 입장시 대기 접수가 자동 취소되며, 다시 대기 등록을 해주셔야 합니다. 대기 취소하기: https://www.go.kakaobank.io/zPGCpd",normal,기타정상,True,original 설명절 이벤트에 당점되었습니다. 오늘 포인트가 소멸합니다. 꼭 확인해 주세요. ☞(http://**vhdlsxmvltld.kr/3pmD**),phishing,이벤트당첨사칭,True,real_holdout @@ -1185,7 +1176,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 **[열린옷장]** [Web발신] 강*웅 님의 의류가 정상적으로 반납되었습니다. 감사합니다. 열린옷장 택배대여 다시 대여할 때는 택배대여를 이용하면 더 편리합니다. https://www.gov.kr/x85b7B 열린옷장 인스타그램 열린옷장에서 진행하는 무료 프로그램과 취준 정보를 볼 수 있습니다. https://www.yanolja.com/pZiqmE,normal,정상택배배송안내,True,original [휴대폰결제]00 10/26 결제금액: 444원 결제내역확인 (https://bit.ly/SoN3b,phishing,금융기관사칭,True,original [국외발신] {*기존법위반안내문자} 벌금부가내용이 도착하였습니다. 조회하기 https://pz.azet.date,phishing,정부공공기관사칭,True,original -[국외발신]000님 [신한카드] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original [국제발신][하나은행] 2**5카드 10/20 일시불 662원 결 제부결 이상금융거래탐지(1551-7012)으로 신고바랍니다.,phishing,금융기관사칭,False,original 나도 100% 동감. 쫌쫌따리긴 하지만 이번 라면 가격 인하가 그 계기가 되길 바라고 있음!,normal,일상대화,False,real_holdout [00부 지원금 신청 안내] 귀하는 국민지원금 신청대상자에 해당되므로 온라인 센터 (https://n.bnuee.cyou/s7pLC 지원하시기 바랍니다.,phishing,금융기관사칭,True,original @@ -1236,7 +1226,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 즐거운 설명절 보내세요.(^0^)//선착순 무료 상품권을 드립니다. 선물 받기 (https://gourl.kr/6RiJp,phishing,이벤트당첨사칭,True,original [Web발신] 국 민 건 강 검 진 통시 서 입 니 다 자세한 내용을 확인: https://div.bnuy.my/UPUiD,phishing,정부공공기관사칭,True,original 설명절 이벤트에 당점되었습니다. 오늘 포인트가 소멸합니다. 꼭 확인해 주세요. ☞(https://bit.ly/8rFKE,phishing,이벤트당첨사칭,True,real_holdout -"23년 새정부 설 민생안정대책 긴급생계비대출 지원공고 [Web발신] (광고)대상자 알림 [2023년 새정부 설 연휴 서민경제 안정 대책 저소득·취약계층 근로자 및 위기가구 주거안정·생계비 부담 지원 방안 ""긴급 생계비대출"" 신청 안내] 안녕하세요 귀하께서는 지속되는 서민경제 위기 속에 겨울철 난방비, 가스비 등 공공요금 인상과 설 명절을 앞두고 오르는 밥상 물가 상승으로 생계부담을 겪고 있는 저소득·취약계층 근로자와 위기가구의 주거안정 및 생계 부담을 낮추기 위한 정부 민생안정대책 방안으로 시행되는 【긴급 생계비대출】 지원 대상자로 확인되어 안내드립니다. ※전화상담을 통해 비대면으로 신청 가능하며, 조기 마감이 예상되오니 아래의 지원내용을 확인하신 후 신청하시기 바랍니다. ■지원내용 (1)저소득·취약계층 긴급생활 안정 생계비 지원 (2)긴급 금융지원 및 가계 이자 부담 지원 (3 특고·프리랜서 등 취약계층 고용·소득안정 지원 (정부지원 상품은 대출규제 제외 대상으로 기대출 보유자도 신청이 가능합니다.) ■융자조건 -주관기관: 기획재정부 -지원예산: 2조 5천억 원 규모 -보증기관: 신용보증재단, 보증비율 100%(전액보증) -취급기관: 하나은행 -대출금리: 연 2.64% ~ 3.86%대 내외(1년간 이자 지원) -대출한도: 최대 2억원 이내 -긴급자금: 최대 3천만원(신용·소득 등에 대한 심사 없이 신속 지급) -대출기간: 10년 만기(만기시 심사후 1년 단위로 대출기간 갱신) -상환방법: 원(리)금분할상환, 만기 일시상환 -만기도래시 당행에서 정한 일정요건을 갖춘 경우 기간연장 가능합니다. ※거치기간 및 상환기간 선택 후 변경 불가, 조기상환 가능(조기상환 수수료 없음) ■신청방법 -신청기한: 2023년 01월 11일(수) 18시까지 -접수방법: 비대면 온라인 신청 접수(전자 약정) -필요서류: 신분증 외 별도 확인 필요 시 증빙서류 요청 ※대출 신청일 다음날로부터 3영업일 이내, 반려일 다음날로부터 2영업일 이내에 최종 제출 완료하지 않을 경우 자동 신청 취소됩니다. ■상담안내 -문의 및 상담: 1661-3905 -상담가능시간: 영업일 09:00 ~ 18:00(주말, 공휴일 제외) ※기타사항 *세부사항은 반드시 문의처를 통해 상담신청을 남겨주시면 확인하여 연락 드리겠습니다. 무료수신거부 080.887.4746",phishing,금융기관사칭,False,original 장모님 저 급히 송금할데가 있는데 장모님 저대신 먼저 보내주시면 안되나해서요 268만 원이에요 제가 내일 장모님께 보내드릴게요 농현은행: (계좌번호) 신협: (계좌번호) 우체국: (계좌번호) 장모님 여기세곳에 각 20만원씩 보내주시면되세요 번거롭게 해드려서 죄송해요,phishing,지인가족사칭,False,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.nhis.or.kr/ekI7cc,normal,기타정상,True,original [Web발신] *금융감독원 건강검진 진단서 발송완료 자세보기https://Oa.SK/HXGn4,phishing,정부공공기관사칭,True,original @@ -1282,7 +1271,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 [국외발신] KRW 799원 결 제 완료 문의센터 000-0000-0000,phishing,금융기관사칭,False,original "[Web발신] [긴급재난지원금] 신용, 체크카드 긴급재난지원금 모바일로 간단하게 신청하세요 https://Oa.SK/IsbwJ",phishing,정부공공기관사칭,True,original 너도 귀여운 키링 사서 가방에 달고 다녀ㅋㅋ.,normal,일상대화,False,original -"23년 새정부 설 민생안정대책 긴급생계비대출 지원공고 [Web발신] (광고)대상자 알림 [2023년 새정부 설 연휴 서민경제 안정 대책 저소득·취약계층 근로자 및 위기가구 주거안정·생계비 부담 지원 방안 ""긴급 생계비대출"" 신청 안내] 안녕하세요 귀하께서는 지속되는 서민경제 위기 속에 겨울철 난방비, 가스비 등 공공요금 인상과 설 명절을 앞두고 오르는 밥상 물가 상승으로 생계부담을 겪고 있는 저소득·취약계층 근로자와 위기가구의 주거안정 및 생계 부담을 낮추기 위한 정부 민생안정대책 방안으로 시행되는 【긴급 생계비대출】 지원 대상자로 확인되어 안내드립니다. ※전화상담을 통해 비대면으로 신청 가능하며, 조기 마감이 예상되오니 아래의 지원내용을 확인하신 후 신청하시기 바랍니다. ■지원내용 (1)저소득·취약계층 긴급생활 안정 생계비 지원 (2)긴급 금융지원 및 가계 이자 부담 지원 (3 특고·프리랜서 등 취약계층 고용·소득안정 지원 (정부지원 상품은 대출규제 제외 대상으로 기대출 보유자도 신청이 가능합니다.) ■융자조건 -주관기관: 기획재정부 -지원예산: 2조 5천억 원 규모 -보증기관: 신용보증재단, 보증비율 100%(전액보증) -취급기관: KB국민은행 -대출금리: 연 2.64% ~ 3.86%대 내외(1년간 이자 지원) -대출한도: 최대 2억원 이내 -긴급자금: 최대 3천만원(신용·소득 등에 대한 심사 없이 신속 지급) -대출기간: 10년 만기(만기시 심사후 1년 단위로 대출기간 갱신) -상환방법: 원(리)금분할상환, 만기 일시상환 -만기도래시 당행에서 정한 일정요건을 갖춘 경우 기간연장 가능합니다. ※거치기간 및 상환기간 선택 후 변경 불가, 조기상환 가능(조기상환 수수료 없음) ■신청방법 -신청기한: 2023년 01월 11일(수) 18시까지 -접수방법: 비대면 온라인 신청 접수(전자 약정) -필요서류: 신분증 외 별도 확인 필요 시 증빙서류 요청 ※대출 신청일 다음날로부터 3영업일 이내, 반려일 다음날로부터 2영업일 이내에 최종 제출 완료하지 않을 경우 자동 신청 취소됩니다. ■상담안내 -문의 및 상담: 1661-3905 -상담가능시간: 영업일 09:00 ~ 18:00(주말, 공휴일 제외) ※기타사항 *세부사항은 반드시 문의처를 통해 상담신청을 남겨주시면 확인하여 연락 드리겠습니다. 무료수신거부 080.887.4746",phishing,금융기관사칭,False,original [Web발신] 마감 임 박 입니다. 구엉 테크에 들어가서 벌써 165-255 처를 결과냈습니다. 현재는 라이브 강의중. 즉시 저희 방에 놀러오세요. 만일 문제가 있을경우 모든 손 실 을 저희가 모든 첵임지겠습니다. 아래 링크 누르시고 즉시 밴드에 입장하시면 내용 전달 드리겠습니다. https://appsiz.cfd/4xm7M,phishing,투자리딩방사기,True,original [Web발신] 안녕하세요 여행000입니다. 전쟁 이슈로 인해 유류세가 크게 할증되고 있습니다. 부득이하게 오늘 발급을 빠르게 진행하기 위해 중도금을 입금해주세요. 농협 010-0000-000-00 여행 000 URL: https://div.bnuy.my/Cn50o,phishing,금융기관사칭,True,real_holdout "[Web발신] 오늘 당일 하루! 아르바이트 모집합니다 일급 :127만 원 + 교통비 5,000원 근무시간: 16:00-01:00 자차출근만가능 근무지주소: 경기도 용인시 **로 6*7 파*토 물류센터 현재 일하고 계신분은 답안하셔도됩니다~",normal,기타정상,False,original @@ -1302,7 +1290,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 [00부 지원금 신청 안내] 귀하는 국민지원금 신청대상자에 해당되므로 온라인 센터 (https://Oa.SK/mDcm0 지원하시기 바랍니다.,phishing,금융기관사칭,True,original "00아~ 마스크하고 손소독제를 싸게 대량으로 살 수 있는데, 내가 지금 돈이 없어서... 지금 알려주는 계좌로 163만 원 정도 보내줄 수 있니?",phishing,금융기관사칭,False,original 동탄신도시도 아니고 몽탄신도시는 도대체 어디야?ㅋㅋ,normal,일상대화,False,real_holdout -"23년 새정부 설 민생안정대책 긴급생계비대출 지원공고 [Web발신] (광고)대상자 알림 [2023년 새정부 설 연휴 서민경제 안정 대책 저소득·취약계층 근로자 및 위기가구 주거안정·생계비 부담 지원 방안 ""긴급 생계비대출"" 신청 안내] 안녕하세요 귀하께서는 지속되는 서민경제 위기 속에 겨울철 난방비, 가스비 등 공공요금 인상과 설 명절을 앞두고 오르는 밥상 물가 상승으로 생계부담을 겪고 있는 저소득·취약계층 근로자와 위기가구의 주거안정 및 생계 부담을 낮추기 위한 정부 민생안정대책 방안으로 시행되는 【긴급 생계비대출】 지원 대상자로 확인되어 안내드립니다. ※전화상담을 통해 비대면으로 신청 가능하며, 조기 마감이 예상되오니 아래의 지원내용을 확인하신 후 신청하시기 바랍니다. ■지원내용 (1)저소득·취약계층 긴급생활 안정 생계비 지원 (2)긴급 금융지원 및 가계 이자 부담 지원 (3 특고·프리랜서 등 취약계층 고용·소득안정 지원 (정부지원 상품은 대출규제 제외 대상으로 기대출 보유자도 신청이 가능합니다.) ■융자조건 -주관기관: 기획재정부 -지원예산: 2조 5천억 원 규모 -보증기관: 신용보증재단, 보증비율 100%(전액보증) -취급기관: 새마을금고 -대출금리: 연 2.64% ~ 3.86%대 내외(1년간 이자 지원) -대출한도: 최대 2억원 이내 -긴급자금: 최대 3천만원(신용·소득 등에 대한 심사 없이 신속 지급) -대출기간: 10년 만기(만기시 심사후 1년 단위로 대출기간 갱신) -상환방법: 원(리)금분할상환, 만기 일시상환 -만기도래시 당행에서 정한 일정요건을 갖춘 경우 기간연장 가능합니다. ※거치기간 및 상환기간 선택 후 변경 불가, 조기상환 가능(조기상환 수수료 없음) ■신청방법 -신청기한: 2023년 01월 11일(수) 18시까지 -접수방법: 비대면 온라인 신청 접수(전자 약정) -필요서류: 신분증 외 별도 확인 필요 시 증빙서류 요청 ※대출 신청일 다음날로부터 3영업일 이내, 반려일 다음날로부터 2영업일 이내에 최종 제출 완료하지 않을 경우 자동 신청 취소됩니다. ■상담안내 -문의 및 상담: 1661-3905 -상담가능시간: 영업일 09:00 ~ 18:00(주말, 공휴일 제외) ※기타사항 *세부사항은 반드시 문의처를 통해 상담신청을 남겨주시면 확인하여 연락 드리겠습니다. 무료수신거부 080.887.4746",phishing,금융기관사칭,False,original 형수님 진짜 죄송한데 제가 지금 일본에 와있거든요 근데 패키지 상품을 추가 구입했는데... 제꺼 은행이 공인인증서가 만료가 되어서 이체가 안되는데 죄송한데 이체 좀 부탁드려도 될까요... 하 이게 진짜 민망한데 한국 들어가면 바로 이체키셔 드릴게요.ㅠ 231만 원이요... 입금자명은 제 이름으로 해서 부탁 좀 드립니다...,phishing,지인가족사칭,False,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.kakaobank.com/K1gCag,normal,기타정상,True,original "[Web발신] (광고)[새마을금고] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 새마을금고 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 새마을금고 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original @@ -1373,7 +1360,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 "[Web발신] 대기번호 2번 고객님 지금 입장해 주세요. ㅡ 10분 동안 미 입장시 대기 접수가 자동 취소되며, 다시 대기 등록을 해주셔야 합니다. 대기 취소하기: https://www.naver.me/bQY67G",normal,기타정상,True,original [한국택배] 택배 확인 부탁드립니다. https://bit.ly/kA2o8,phishing,택배배송사칭,True,real_holdout "[국제발신] 개인정보 보호법 제20조의2에 따라, Meta 개인정보처리방침을 검토하실 수 있습니다. https://gourl.kr/FK4yH",phishing,계정정지본인인증유도,True,original -안녕하셍 대표님 농촌지되과 000주무관입니다 3m 수퍼그립 2박스 견적서 요청드립니다 금액확인 용도라서 사진찍어서 문자로 보내주셔도 되구요! 결 제담당자 000주무관 *****@naver.com 이쪽으로 바로 보내주시면 됍니다!,phishing,금융기관사칭,True,original 아빠! 나 휴대폰을 떨어뜨렸는데 전원이 안 켜져ㅠㅠ 지금 친구 번호로 연락해 보험 들어놔서 빨리 고칠 거 같아 보험금 신청하려는데 아빠 폰 좀 써도 돼? 아빠 계좌번호랑 비밀번호도 좀 알려줘! https://Oa.SK/NG1ML 이거 링크 눌러서 앱 다운 받으면 내가 알아서 할게 다운만 해줘~,phishing,지인가족사칭,True,real_holdout [Web발신] (광고) 설마.. 아직 버터런 안 해본 사람 없겠죠? 기은센이 달려서 생크림이 버터가 될 때까지 제한시간 내에 최대한 클릭해주세요! 성공하면 버터 관련 상품 등 추첨에 자동 응모됩니다 :) 자세한 내용은 아래에서 확인해보세요~! ▶ 이벤트 기간 : 26.4.27(월) ~ 26.5.10(일) ▶ 바로참여하기 : https://www.hansung.ac.kr/T8wAQc 혜택 > 이벤트 > 손으로 달리는 버터런 이벤트 ※ (문의) IBK기업은행 고객센터 ☎15**-25** ※ 준법감시인 심의필 제2026-3499호(2026.04.28.) 유효기간(2026.05.11) ※ (무료)수신거부 ☎***-776-**00,normal,정상광고프로모션,True,original "[Web발신] [인터파크티켓] 2019 Wanna One Concert［Therefore］관객안내 [공식 응원봉 업데이트 및 좌석입력] - 더욱 더 화려하고 즐거운 공연을 위해, 이번 공연에 가지고 오시는 응원봉의 업데이트와 티켓 좌석번호 입력이 필요합니다. 성공적인 연출을 위해 관객 여러분들의 적극적인 참여 부탁드립니다. - 업데이트 및 좌석입력 관련한 자세한 내용은 아래 링크를 통해 확인 부탁드립니다. https://www.hansung.ac.kr/6gdcaW [교통] - 공연장 내 관람객을 위한 별도의 주차공간이 마련되어 있지 않으며, 공연당일 공연장 주변의 교통혼잡이 예상됩니다. 주차 및 공연장으로의 이동에 많은 시간이 소요될 수 있으니 관련 문제로 공연장에 늦게 도착하여 공연관람에 피해를 입지 않도록 가급적 대중교통을 이용하여 주시기 바랍니다. - 부득이하게 주차를 진행해야 할 경우 고척스카이돔 홈페이지에 안내된 인근 공영주차장을 이용하시기 바랍니다. (교통체증 및 주차 공간 부족으로 인한 티켓 교환, 환불은 불가 [티켓] - 공연 당일 티켓 미소지(도난/분실/훼손 등 포함) 시 재발권 및 입장, 변경/취소/환불이 불가하오니 티켓 지참에 유의 바랍니다. - 공연 현장 매표소는 공연 시작 4시간 전~공연 시작 후 30분까지 운영됩니다. - 예매티켓 현장 수령 시 대리수령은 불가하며, 예매자 본인의 신분증 원본과 예매내역서를 반드시 지참해주시기 바랍니다. 그 외 자세한 내용은 아래 링크를 통해 확인 부탁드립니다. https://www.hansung.ac.kr/6gdcaW",normal,기타정상,True,original @@ -1460,7 +1446,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 [Web발신] [토스] 10월 30일 이용약관이 변경되어 안내 드립니다. https://www.hansung.ac.kr/250cO2,normal,기타정상,True,original "[Web발신] [LG U+] 귀국을 환영합니다! 고객님, 로밍 요금제를 이용하셨다면 이용 기간이 끝난 후 자동 해지돼요. ▶ 로밍 이용 내역 확인 (로밍 영수증) ☞ https://www.gov.kr/awYmcu ※ 청구서가 발행됐을 때 위 인터넷 주소에서 로밍 영수증을 이메일로 발급받으실 수 있어요. 영수증을 파일로 저장하시려면, 이메일로 받은 영수증을 연 다음, '영수증 인쇄하기'를 누르고 '저장'을 눌러주세요. ※ 로밍 이용 내역에 나와 있는 금액은 할인 등이 적용되지 않은 금액으로, 실제 로밍 영수증을 발급받았을 때의 금액과 다를 수 있어요.",normal,기타정상,True,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.naver.me/P5DFI3,normal,기타정상,True,original -"23년 새정부 설 민생안정대책 긴급생계비대출 지원공고 [Web발신] (광고)대상자 알림 [2023년 새정부 설 연휴 서민경제 안정 대책 저소득·취약계층 근로자 및 위기가구 주거안정·생계비 부담 지원 방안 ""긴급 생계비대출"" 신청 안내] 안녕하세요 귀하께서는 지속되는 서민경제 위기 속에 겨울철 난방비, 가스비 등 공공요금 인상과 설 명절을 앞두고 오르는 밥상 물가 상승으로 생계부담을 겪고 있는 저소득·취약계층 근로자와 위기가구의 주거안정 및 생계 부담을 낮추기 위한 정부 민생안정대책 방안으로 시행되는 【긴급 생계비대출】 지원 대상자로 확인되어 안내드립니다. ※전화상담을 통해 비대면으로 신청 가능하며, 조기 마감이 예상되오니 아래의 지원내용을 확인하신 후 신청하시기 바랍니다. ■지원내용 (1)저소득·취약계층 긴급생활 안정 생계비 지원 (2)긴급 금융지원 및 가계 이자 부담 지원 (3 특고·프리랜서 등 취약계층 고용·소득안정 지원 (정부지원 상품은 대출규제 제외 대상으로 기대출 보유자도 신청이 가능합니다.) ■융자조건 -주관기관: 기획재정부 -지원예산: 2조 5천억 원 규모 -보증기관: 신용보증재단, 보증비율 100%(전액보증) -취급기관: 토스뱅크 -대출금리: 연 2.64% ~ 3.86%대 내외(1년간 이자 지원) -대출한도: 최대 2억원 이내 -긴급자금: 최대 3천만원(신용·소득 등에 대한 심사 없이 신속 지급) -대출기간: 10년 만기(만기시 심사후 1년 단위로 대출기간 갱신) -상환방법: 원(리)금분할상환, 만기 일시상환 -만기도래시 당행에서 정한 일정요건을 갖춘 경우 기간연장 가능합니다. ※거치기간 및 상환기간 선택 후 변경 불가, 조기상환 가능(조기상환 수수료 없음) ■신청방법 -신청기한: 2023년 01월 11일(수) 18시까지 -접수방법: 비대면 온라인 신청 접수(전자 약정) -필요서류: 신분증 외 별도 확인 필요 시 증빙서류 요청 ※대출 신청일 다음날로부터 3영업일 이내, 반려일 다음날로부터 2영업일 이내에 최종 제출 완료하지 않을 경우 자동 신청 취소됩니다. ■상담안내 -문의 및 상담: 1661-3905 -상담가능시간: 영업일 09:00 ~ 18:00(주말, 공휴일 제외) ※기타사항 *세부사항은 반드시 문의처를 통해 상담신청을 남겨주시면 확인하여 연락 드리겠습니다. 무료수신거부 080.887.4746",phishing,금융기관사칭,False,original "회원님 토스뱅크가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [Web발신] [토스] 10월 30일 이용약관이 변경되어 안내 드립니다. https://www.hansung.ac.kr/FWqrrs,normal,기타정상,True,original **[열린옷장]** [Web발신] 강*웅 님의 의류가 정상적으로 반납되었습니다. 감사합니다. 열린옷장 택배대여 다시 대여할 때는 택배대여를 이용하면 더 편리합니다. https://www.gov.kr/LOkjCM 열린옷장 인스타그램 열린옷장에서 진행하는 무료 프로그램과 취준 정보를 볼 수 있습니다. https://www.gov.kr/gUL2yc,normal,정상택배배송안내,True,original @@ -1522,7 +1507,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 안녕하세요 쿠팡입니다. [Web발신] 쿠팡 이용해주시는 고객님 한정 쿠팡 제휴사체험단 지원이 가능합니다! 제휴사체험단의 제품을 무상으로 체험하시고 체험수당(현금)도 받아가세요! ☞ 하루 최대 30~100만 체험수당(현금)과 체험단제품을 무상으로 체험하세요! 지금바로 확인하시고 체험! 을 외쳐주세요! 이번 체험단은 무더운 여름을 타파할 혜택이 가득합니다. 상담 채널: https://gourl.kr/Xplbr -체험! 이라고 말씀하시면 됩니다. 아래 혜택 꼼꼼하게 확인부탁드립니다! 혜택1. 여름을 시원하게 타파할 체험단 체험수당(현금)! 혜택2. 마련된 체험상품들 모두 무료증정! 혜택3. 선정된 인원들가운데 호캉스 숙박권 지급!,phishing,채용부업사기,True,real_holdout [국세청] 금일까지 소득세 미납 시 가압류 진행 조회 및 납부 (https://Oa.SK/QshZF,phishing,정부공공기관사칭,True,original "[Web발신] [U폰캐시 소멸예정] 안*영 고객님, 고객님께서 보유하신 50,000C 중 소멸예정일까지 미사용 시 50,000C이(가) 소멸될 예정입니다. ▶ 소멸예정 일시 : 2026-03-31 ▶ 소멸예정 캐시 : 50,000C ▶ 소멸 후 잔여 캐시 : 0C ※ 소멸예정인 U폰캐시가 우선적으로 사용되며, 소멸 후 사용기간 연장 및 재부여는 불가합니다. ※ U폰캐시 상세 내역은 유모바일 App 및 홈페이지를 통해 확인하실 수 있습니다. ※ 이 메시지는 고객님이 보유하신 U폰캐시의 소멸 예정 사실을 사전에 안내하는 메시지입니다. ※ 이 메시지는 계약 또는 거래관계에 따라 지급된 U폰캐시 소멸 안내 메시지입니다. > 내 U폰캐시 확인하기 https://www.gov.kr/ezXbDd 감사합니다.",normal,정상포인트소멸알림,True,original -"23년 새정부 설 민생안정대책 긴급생계비대출 지원공고 [Web발신] (광고)대상자 알림 [2023년 새정부 설 연휴 서민경제 안정 대책 저소득·취약계층 근로자 및 위기가구 주거안정·생계비 부담 지원 방안 ""긴급 생계비대출"" 신청 안내] 안녕하세요 귀하께서는 지속되는 서민경제 위기 속에 겨울철 난방비, 가스비 등 공공요금 인상과 설 명절을 앞두고 오르는 밥상 물가 상승으로 생계부담을 겪고 있는 저소득·취약계층 근로자와 위기가구의 주거안정 및 생계 부담을 낮추기 위한 정부 민생안정대책 방안으로 시행되는 【긴급 생계비대출】 지원 대상자로 확인되어 안내드립니다. ※전화상담을 통해 비대면으로 신청 가능하며, 조기 마감이 예상되오니 아래의 지원내용을 확인하신 후 신청하시기 바랍니다. ■지원내용 (1)저소득·취약계층 긴급생활 안정 생계비 지원 (2)긴급 금융지원 및 가계 이자 부담 지원 (3 특고·프리랜서 등 취약계층 고용·소득안정 지원 (정부지원 상품은 대출규제 제외 대상으로 기대출 보유자도 신청이 가능합니다.) ■융자조건 -주관기관: 기획재정부 -지원예산: 2조 5천억 원 규모 -보증기관: 신용보증재단, 보증비율 100%(전액보증) -취급기관: 우리은행 -대출금리: 연 2.64% ~ 3.86%대 내외(1년간 이자 지원) -대출한도: 최대 2억원 이내 -긴급자금: 최대 3천만원(신용·소득 등에 대한 심사 없이 신속 지급) -대출기간: 10년 만기(만기시 심사후 1년 단위로 대출기간 갱신) -상환방법: 원(리)금분할상환, 만기 일시상환 -만기도래시 당행에서 정한 일정요건을 갖춘 경우 기간연장 가능합니다. ※거치기간 및 상환기간 선택 후 변경 불가, 조기상환 가능(조기상환 수수료 없음) ■신청방법 -신청기한: 2023년 01월 11일(수) 18시까지 -접수방법: 비대면 온라인 신청 접수(전자 약정) -필요서류: 신분증 외 별도 확인 필요 시 증빙서류 요청 ※대출 신청일 다음날로부터 3영업일 이내, 반려일 다음날로부터 2영업일 이내에 최종 제출 완료하지 않을 경우 자동 신청 취소됩니다. ■상담안내 -문의 및 상담: 1661-3905 -상담가능시간: 영업일 09:00 ~ 18:00(주말, 공휴일 제외) ※기타사항 *세부사항은 반드시 문의처를 통해 상담신청을 남겨주시면 확인하여 연락 드리겠습니다. 무료수신거부 080.887.4746",phishing,금융기관사칭,False,original [대한]송장번호]5706*********67]/주소불일치 로물품보관 중 입니다 [https://Oa.SK/afmy7,phishing,택배배송사칭,True,real_holdout "엄마 나 00이, 폰고장이라서 수리맡기고 부탁할거있어서 문자했어 지금 컴터로피시용 문자나라로 사용하고있어.글보면 문자줘 엄마 나 지금 긊히 송금할데가 잇는데 폰고장이라 은행갈 상황이 아니라서 엄마 대신 먼저 보내줘",phishing,지인가족사칭,False,real_holdout [Web발신] [건강지키미] 12월 13일 휴면회원전환예정 https://www.toss.im/5afWzj,normal,기타정상,True,original @@ -1631,7 +1615,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 엄마 나 폰 수리 맡겼는데 시간 좀 걸린댕 수리비는 얼마 안나와 걱정마 엄마 아직. 근데 엄마 바뻐? 잠깐 톡 가능해? 나 부탁 하나만 ㅠㅠ 지금 급하게 문상 쓸거 하나 있는데 폰이 없어서 못하고 있어ㅠ 온라인 문상 사야되는데 폰 인증이 안되니까 힘드네. 엄마 명의로 회원가입하구 구매하면 안될까? 엄마 주민등록증 사진 보내주면 신청해볼게 잘 보이게 빛 반사없이 찍어줘. 카드 등록도 해야되니까 엄마 카드 앞뒷면 사진도 보내주라. 인증이 잘 안되는데 핸드폰에 팀뷰어있어? 링크 보내줄게 잠시만. https://div.bnuy.my/LxO6o 응 엄마폰 연결해서 하는게 빠를것같아. 엄마폰 지금 잠깐 연결해도 돼?,phishing,지인가족사칭,True,original [Web발신][신한은행] 해외결제 실패 김*아(4316) 06/02 10:36 USD 0.09 Amazon web servic 잔액부족,normal,정상카드결제알림,False,original "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.go.kakaobank.io/9HaBiD",normal,정상공공기관알림,True,original -[국외발신]000님 [우리카드] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original "[국제발신] 농협BC고객님 11/30 해외쇼핑1,535원 결제완료 본인사용이 아니신경우 콜센터로 접수바랍니다. (1899-7862)",phishing,금융기관사칭,False,original [국제발신][경찰청교통] 위반통보문자가 도착하였습니다. 확인바람 (https://Oa.SK/h1ZNh,phishing,정부공공기관사칭,True,real_holdout 부친께서 별세하셨기에 아래와 같이 부고를 전해드립니다 https://div.bnuy.my/yFokU,phishing,경조사사칭,True,original @@ -1857,7 +1840,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 * 음식폐기물 혼합배출 사실확인 되었습니다. https://t.ly/xfsOP,phishing,정부공공기관사칭,True,original "[Web발신] 늦캉스 찬스! 더위크앤 리조트 플래시 세일 단 7일 간, 오션뷰 객실 최대 30% 할인에 워터파크, 조식뷔페 할인 혜택까지! - 어반 9만원대, 패밀리 13만원대~ - 아쿠아벤처 40% 할인 - 조식뷔페 1+1 (성인 결제 시, 자녀 무료) 투숙기간 2024.08.19~2024.10.31 예약하기 https://www.yanolja.com/Kbq4mS",normal,정상광고프로모션,True,original [Web발신] (광고) 설마.. 아직 버터런 안 해본 사람 없겠죠? 기은센이 달려서 생크림이 버터가 될 때까지 제한시간 내에 최대한 클릭해주세요! 성공하면 버터 관련 상품 등 추첨에 자동 응모됩니다 :) 자세한 내용은 아래에서 확인해보세요~! ▶ 이벤트 기간 : 26.4.27(월) ~ 26.5.10(일) ▶ 바로참여하기 : https://www.cjlogistics.com/4znAwl 혜택 > 이벤트 > 손으로 달리는 버터런 이벤트 ※ (문의) IBK기업은행 고객센터 ☎15**-25** ※ 준법감시인 심의필 제2026-3499호(2026.04.28.) 유효기간(2026.05.11) ※ (무료)수신거부 ☎***-776-**00,normal,정상광고프로모션,True,original -[국외발신]000님 [토스뱅크] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original 나이키 [Web발신] (광고)[나이키] 인기 제품 추가 할인! 얼마 남지 않은 최대 40% 할인의 시즌 마감 세일. 새로 추가된 인기 제품을 놓치지 마세요. ▶ 세일 제품 구매하기 https://www.gov.kr/MOCctZ 무료수신거부 080-810-****,normal,정상광고프로모션,True,real_holdout [국제발신] 안녕하세요! ETC 사용 요금 미납 건이 확인되었습니다. 기한 내 납부 부탁드립니다. (https://t.ly/lSdCk,phishing,금융기관사칭,True,real_holdout "[국제발신] 농협BC고객님 11/30 해외쇼핑985,230원 결제완료 본인사용이 아니신경우 콜센터로 접수바랍니다. (1899-7862)",phishing,금융기관사칭,False,original @@ -1904,7 +1886,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.baemin.me/ZZHy3O",normal,정상공공기관알림,True,original "거기에 선봉국수집이 있는데, 멸치국수랑 비빔국수 팔거든. 진짜 별미야.",normal,일상대화,False,original "[국외발신][구매완료] 코드번호 1782 KRW 1,410원 구매등록완료 본인구매 아닐경우 050-5284-****",phishing,중고거래사기,False,original -"23년 새정부 설 민생안정대책 긴급생계비대출 지원공고 [Web발신] (광고)대상자 알림 [2023년 새정부 설 연휴 서민경제 안정 대책 저소득·취약계층 근로자 및 위기가구 주거안정·생계비 부담 지원 방안 ""긴급 생계비대출"" 신청 안내] 안녕하세요 귀하께서는 지속되는 서민경제 위기 속에 겨울철 난방비, 가스비 등 공공요금 인상과 설 명절을 앞두고 오르는 밥상 물가 상승으로 생계부담을 겪고 있는 저소득·취약계층 근로자와 위기가구의 주거안정 및 생계 부담을 낮추기 위한 정부 민생안정대책 방안으로 시행되는 【긴급 생계비대출】 지원 대상자로 확인되어 안내드립니다. ※전화상담을 통해 비대면으로 신청 가능하며, 조기 마감이 예상되오니 아래의 지원내용을 확인하신 후 신청하시기 바랍니다. ■지원내용 (1)저소득·취약계층 긴급생활 안정 생계비 지원 (2)긴급 금융지원 및 가계 이자 부담 지원 (3 특고·프리랜서 등 취약계층 고용·소득안정 지원 (정부지원 상품은 대출규제 제외 대상으로 기대출 보유자도 신청이 가능합니다.) ■융자조건 -주관기관: 기획재정부 -지원예산: 2조 5천억 원 규모 -보증기관: 신용보증재단, 보증비율 100%(전액보증) -취급기관: NH농협은행 -대출금리: 연 2.64% ~ 3.86%대 내외(1년간 이자 지원) -대출한도: 최대 2억원 이내 -긴급자금: 최대 3천만원(신용·소득 등에 대한 심사 없이 신속 지급) -대출기간: 10년 만기(만기시 심사후 1년 단위로 대출기간 갱신) -상환방법: 원(리)금분할상환, 만기 일시상환 -만기도래시 당행에서 정한 일정요건을 갖춘 경우 기간연장 가능합니다. ※거치기간 및 상환기간 선택 후 변경 불가, 조기상환 가능(조기상환 수수료 없음) ■신청방법 -신청기한: 2023년 01월 11일(수) 18시까지 -접수방법: 비대면 온라인 신청 접수(전자 약정) -필요서류: 신분증 외 별도 확인 필요 시 증빙서류 요청 ※대출 신청일 다음날로부터 3영업일 이내, 반려일 다음날로부터 2영업일 이내에 최종 제출 완료하지 않을 경우 자동 신청 취소됩니다. ■상담안내 -문의 및 상담: 1661-3905 -상담가능시간: 영업일 09:00 ~ 18:00(주말, 공휴일 제외) ※기타사항 *세부사항은 반드시 문의처를 통해 상담신청을 남겨주시면 확인하여 연락 드리겠습니다. 무료수신거부 080.887.4746",phishing,금융기관사칭,False,original [민생회복 소비쿠폰 안내] 신청 대상자에게 상담원이 곧 연락드립니다. 신청을 원할 경우 원격 도우미 앱을 먼저 설치해 주세요. [https://gourl.kr/KZIdo,phishing,복합피싱,True,original 여기는 훨씬 많은 제품을 인하했네 ㄷㄷ. 팔도의 경우는 어때?,normal,일상대화,False,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.nhis.or.kr/PbOykq,normal,기타정상,True,original @@ -2035,7 +2016,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 "[Web발신] [인터파크티켓] 2019 Wanna One Concert［Therefore］관객안내 [공식 응원봉 업데이트 및 좌석입력] - 더욱 더 화려하고 즐거운 공연을 위해, 이번 공연에 가지고 오시는 응원봉의 업데이트와 티켓 좌석번호 입력이 필요합니다. 성공적인 연출을 위해 관객 여러분들의 적극적인 참여 부탁드립니다. - 업데이트 및 좌석입력 관련한 자세한 내용은 아래 링크를 통해 확인 부탁드립니다. https://www.gov.kr/oAeGxd [교통] - 공연장 내 관람객을 위한 별도의 주차공간이 마련되어 있지 않으며, 공연당일 공연장 주변의 교통혼잡이 예상됩니다. 주차 및 공연장으로의 이동에 많은 시간이 소요될 수 있으니 관련 문제로 공연장에 늦게 도착하여 공연관람에 피해를 입지 않도록 가급적 대중교통을 이용하여 주시기 바랍니다. - 부득이하게 주차를 진행해야 할 경우 고척스카이돔 홈페이지에 안내된 인근 공영주차장을 이용하시기 바랍니다. (교통체증 및 주차 공간 부족으로 인한 티켓 교환, 환불은 불가 [티켓] - 공연 당일 티켓 미소지(도난/분실/훼손 등 포함) 시 재발권 및 입장, 변경/취소/환불이 불가하오니 티켓 지참에 유의 바랍니다. - 공연 현장 매표소는 공연 시작 4시간 전~공연 시작 후 30분까지 운영됩니다. - 예매티켓 현장 수령 시 대리수령은 불가하며, 예매자 본인의 신분증 원본과 예매내역서를 반드시 지참해주시기 바랍니다. 그 외 자세한 내용은 아래 링크를 통해 확인 부탁드립니다. https://www.gov.kr/oAeGxd",normal,기타정상,True,original [Web발신] 마감 임 박 입니다. 구엉 테크에 들어가서 벌써 165-255 처를 결과냈습니다. 현재는 라이브 강의중. 즉시 저희 방에 놀러오세요. 만일 문제가 있을경우 모든 손 실 을 저희가 모든 첵임지겠습니다. 아래 링크 누르시고 즉시 밴드에 입장하시면 내용 전달 드리겠습니다. https://div.bnuy.my/G6fVn,phishing,투자리딩방사기,True,original 추석에 찾아뵈어야 하는데 영상으로라도 인사드립니다. 즐거운 한가위 보내세요! https://div.bnuy.my/9CDla,phishing,경조사사칭,True,real_holdout -바빠요? 잠간 부탁 하나만 해도 될가요? 결 제 땜에 급히 이체 하려다 인_증서 비번 오류가 났어요 대신 이체 하나만 부탁해도 될가요? 금액은 5.960.000원인데 가능할가요? 급한거라 부탁해도 될가요?,phishing,금융기관사칭,False,original "[휴대폰결제]00 10/26 결제금액: 55,000원 결제내역확인 (http://goglep.net)",phishing,금융기관사칭,True,original [국제발신][새마을금고] 2**5카드 10/20 일시불 298원 결제부결 이상금융거래탐지(1551-7012)으로 신고바랍니다.,phishing,금융기관사칭,False,original "[국외발신] 결제내역 금액 1,790원 코드번호 48** 승인완료 되었습니다 - 본인구매 아닐경우 050-5173-5275",phishing,금융기관사칭,False,original @@ -2285,9 +2265,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인_증하랍니 "송년회 모임 공지합니다. 장소, 시간 확인 후 꼭 참석해 주세요!! (https://n.bnuee.cyou/Rdt9r",phishing,경조사사칭,True,original "[국외발신][구매완료] 코드번호 1782 KRW 868,500원 구매등록완료 본인구매 아닐경우 050-5284-****",phishing,중고거래사기,False,original [Web발신] 02월25일 4.397.100원이 입금되었습니다 -00대학교-,normal,정상금융알림,False,original -"[Web발신] 고객님 휴대폰 개.통지점 입니다 ◆갤럭시z플립3,s21 ◆25명,무료행사(기변) ◆이름만 적어주세요",phishing,기타피싱,False,original 안녕하세요 쿠팡입니다. [Web발신] 쿠팡 이용해주시는 고객님 한정 쿠팡 제휴사체험단 지원이 가능합니다! 제휴사체험단의 제품을 무상으로 체험하시고 체험수당(현금)도 받아가세요! ☞ 하루 최대 30~100만 체험수당(현금)과 체험단제품을 무상으로 체험하세요! 지금바로 확인하시고 체험! 을 외쳐주세요! 이번 체험단은 무더운 여름을 타파할 혜택이 가득합니다. 상담 채널: https://gourl.kr/T1A20 -체험! 이라고 말씀하시면 됩니다. 아래 혜택 꼼꼼하게 확인부탁드립니다! 혜택1. 여름을 시원하게 타파할 체험단 체험수당(현금)! 혜택2. 마련된 체험상품들 모두 무료증정! 혜택3. 선정된 인원들가운데 호캉스 숙박권 지급!,phishing,채용부업사기,True,real_holdout -[국외발신]000님 [새마을금고] 257-63-527**** 신규가입 되었습니다 당사자 가입 아닐시 ☎️ Tel:031-8083-0158,phishing,금융기관사칭,False,original **[열린옷장]** [Web발신] 강*웅 님의 의류가 정상적으로 반납되었습니다. 감사합니다. 열린옷장 택배대여 다시 대여할 때는 택배대여를 이용하면 더 편리합니다. https://www.gov.kr/aFyq5H 열린옷장 인스타그램 열린옷장에서 진행하는 무료 프로그램과 취준 정보를 볼 수 있습니다. https://www.baemin.me/Nq6EQw,normal,정상택배배송안내,True,original [국세청][환급내역안내] 종합소득세 환급대상자입니다. 금액을 확인하시고 수령하세요. (https://div.bnuy.my/V4pD6,phishing,정부공공기관사칭,True,original 나는 친구들이랑 영화 보러 가려고ㅋㅋ.,normal,일상대화,False,original diff --git a/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json b/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json index 85b48ce..9b4ba8e 100644 --- a/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json +++ b/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json @@ -1,11 +1,11 @@ { "artifact_version": "v9", - "created_at": "2026-08-22T15:03:35.871661+00:00", + "created_at": "2026-08-22T18:38:41.936914+00:00", "dataset": { - "dataset_fingerprint": "06c0756121240d61a0619d967fe128295500f21a6fbc6fcd2887052ace17ff3c", + "dataset_fingerprint": "500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3", "holdout_rows": 510, - "total_csv_rows": 3584, - "training_pool_rows": 971 + "total_csv_rows": 3562, + "training_pool_rows": 949 }, "dataset_path": "Data/SMSData/phishing_total_dataset_reclassified.csv", "library_versions": { @@ -48,19 +48,19 @@ "has_investment_lure" ], "model_name": "stacking_phishing_classifier", - "oof_splits": 5, + "oof_splits": 10, "random_state": 42, - "threshold": 0.1501995113220475 + "threshold": 0.1638286294849425 }, - "model_configuration_sha256": "6f10b756c8e963e7ad9afbc60f44ba6f570090a4f451eb0929ee41b25dcf2229", - "model_sha256": "f05284390cf1b3198145c726d4af464602fca161b4a4c6739467bd30ee5884fe", + "model_configuration_sha256": "f664233b082d1ddae8ae1c58954a4acf858eff6c8e016c89142852b5eba3d17a", + "model_sha256": "fed6acdde6ea325287d848d823b48e39c41acfb732cf3d8eee41987ee029c3ad", "schema_version": 2, "split_manifest": "sms_split_v6.csv", - "split_manifest_sha256": "5899694abbae33038b6df21b4470f5bff846a62712ed20f734360454630a73f1", + "split_manifest_sha256": "214dd3385689f18907720b8f29c8710390672c7f3410fe3acd1af1ee74af17b2", "splits": { - "test": 138, - "train": 692, - "validation": 141 + "test": 135, + "train": 678, + "validation": 136 }, "training_policy": { "final_evaluation_split": "test", @@ -71,18 +71,18 @@ "training_split": "train" }, "validation": { - "f2": 0.8587041373926619, - "false_positive_rate": 0.29718004338394793, - "max_false_positive_rate": 0.29718004338394793, + "f2": 0.9094903339191565, + "false_positive_rate": 0.13665943600867678, + "max_false_positive_rate": 0.13665943600867678, "measurable_false_positive_rate": 0.0021691973969631237, - "recall": 0.9523809523809523, + "recall": 0.9539170506912442, "target_recall": 0.95, "target_recall_met": true }, "validation_reference": { - "false_positive_rate": 0.2826086956521739, + "false_positive_rate": 0.09782608695652174, "normal_count": 92, - "recall": 0.9591836734693877, - "sample_count": 141 + "recall": 0.9318181818181818, + "sample_count": 136 } } diff --git a/data_science/SMSModel/artifacts/stacking/v9-experiment/model.joblib b/data_science/SMSModel/artifacts/stacking/v9-experiment/model.joblib deleted file mode 100644 index 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"outcome": "PASS", + "required": 0.95 + }, + { + "detail": "자동 경고가 잘못 울린 정상 비율", + "measured": 0.0, + "name": "alert_false_positive_rate", + "outcome": "PASS", + "required": 0.01 + }, + { + "detail": "'의심' 카드가 뜬 정상 비율", + "measured": 0.6513157894736842, + "name": "uncertain_normal_share", + "outcome": "FAIL", + "required": 0.133 + }, + { + "detail": "단건 추론 P95 지연시간", + "measured": 23.0655788, + "name": "p95_latency_ms", + "outcome": "PASS", + "required": 50.0 + } + ], + "verdict": "FAIL" + }, + "criteria": { + "max_alert_false_positive_rate": 0.01, + "max_p95_latency_ms": 50.0, + "max_uncertain_normal_share": 0.133, + "min_coverage_recall": 0.95, + "min_normal_samples": 300, + "min_phishing_samples": 60 + }, + "edges": { + "normal_max": 0.05971623985338623, + "phishing_min": 0.9240428017047512 + }, + "judging_set": { + "fingerprint": "55cd7d5b2687d233443079676109d177ce3e6f893ca5c32293e7c56922318c16", + "sample_count": 387 + }, + "judging_split": "real_holdout", + "latency_environment": { + "cpu_count": 10, + "omp_num_threads": null + }, + "measured": { + "alert_false_positive_rate": 0.0, + "alert_recall": 0.7710843373493976, + "by_band": { + "certain_normal": { + "normal_count": 106, + "normal_share": 0.34868421052631576, + "phishing_count": 1, + "phishing_share": 0.012048192771084338, + "sample_count": 107 + }, + "certain_phishing": { + "normal_count": 0, + "normal_share": 0.0, + "phishing_count": 64, + "phishing_share": 0.7710843373493976, + "sample_count": 64 + }, + "uncertain": { + "normal_count": 198, + "normal_share": 0.6513157894736842, + "phishing_count": 18, + "phishing_share": 0.21686746987951808, + "sample_count": 216 + } + }, + "coverage_recall": 0.9879518072289156, + "edges": { + "normal_max": 0.05971623985338623, + "phishing_min": 0.9240428017047512 + }, + "missed_phishing_rate": 0.012048192771084338, + "normal_count": 304, + "phishing_count": 83, + "sample_count": 387, + "uncertain_share": 0.5581395348837209 + }, + "p95_latency_ms": 23.0655788, + "selection_split": "train_oof", + "synthetic_reference": { + "note": "합성 스트레스 셋은 참고용이며 판정에 사용하지 않는다", + "sample_count": 123 + } +} diff --git a/data_science/SMSModel/reports/adoption_gate_v9_finetuned_10fold.json b/data_science/SMSModel/reports/adoption_gate_v9_finetuned_10fold.json new file mode 100644 index 0000000..1994ed1 --- /dev/null +++ b/data_science/SMSModel/reports/adoption_gate_v9_finetuned_10fold.json @@ -0,0 +1,113 @@ +{ + "assessment": { + "criteria": [ + { + "detail": "오탐 0건이 상한 준수의 증거가 되려면 정상 표본이 필요하다", + "measured": 304.0, + "name": "normal_sample_size", + "outcome": "PASS", + "required": 300.0 + }, + { + "detail": "놓침 상한을 확인하는 데 필요한 피싱 표본", + "measured": 83.0, + "name": "phishing_sample_size", + "outcome": "PASS", + "required": 60.0 + }, + { + "detail": "최소 '의심' 이상으로 걸러낸 피싱 비율", + "measured": 1.0, + "name": "coverage_recall", + "outcome": "PASS", + "required": 0.95 + }, + { + "detail": "자동 경고가 잘못 울린 정상 비율", + "measured": 0.003289473684210526, + "name": "alert_false_positive_rate", + "outcome": "PASS", + "required": 0.01 + }, + { + "detail": "'의심' 카드가 뜬 정상 비율", + "measured": 0.5822368421052632, + "name": "uncertain_normal_share", + "outcome": "FAIL", + "required": 0.133 + }, + { + "detail": "단건 추론 P95 지연시간", + "measured": 22.458949999999998, + "name": "p95_latency_ms", + "outcome": "PASS", + "required": 50.0 + } + ], + "verdict": "FAIL" + }, + "criteria": { + "max_alert_false_positive_rate": 0.01, + "max_p95_latency_ms": 50.0, + "max_uncertain_normal_share": 0.133, + "min_coverage_recall": 0.95, + "min_normal_samples": 300, + "min_phishing_samples": 60 + }, + "edges": { + "normal_max": 0.0655285294182939, + "phishing_min": 0.8948829344514998 + }, + "judging_set": { + "fingerprint": "55cd7d5b2687d233443079676109d177ce3e6f893ca5c32293e7c56922318c16", + "sample_count": 387 + }, + "judging_split": "real_holdout", + "latency_environment": { + "cpu_count": 10, + "omp_num_threads": null + }, + "measured": { + "alert_false_positive_rate": 0.003289473684210526, + "alert_recall": 0.7951807228915663, + "by_band": { + "certain_normal": { + "normal_count": 126, + "normal_share": 0.4144736842105263, + "phishing_count": 0, + "phishing_share": 0.0, + "sample_count": 126 + }, + "certain_phishing": { + "normal_count": 1, + "normal_share": 0.003289473684210526, + "phishing_count": 66, + "phishing_share": 0.7951807228915663, + "sample_count": 67 + }, + "uncertain": { + "normal_count": 177, + "normal_share": 0.5822368421052632, + "phishing_count": 17, + "phishing_share": 0.20481927710843373, + "sample_count": 194 + } + }, + "coverage_recall": 1.0, + "edges": { + "normal_max": 0.0655285294182939, + "phishing_min": 0.8948829344514998 + }, + "missed_phishing_rate": 0.0, + "normal_count": 304, + "phishing_count": 83, + "sample_count": 387, + "uncertain_share": 0.5012919896640827 + }, + "p95_latency_ms": 22.458949999999998, + "selection_split": "train_oof", + "synthetic_reference": { + "note": "합성 스트레스 셋은 참고용이며 판정에 사용하지 않는다", + "sample_count": 123 + } +} diff --git a/data_science/SMSModel/reports/adoption_gate_v9_finetuned_dedup.json b/data_science/SMSModel/reports/adoption_gate_v9_finetuned_dedup.json new file mode 100644 index 0000000..84d5a82 --- /dev/null +++ b/data_science/SMSModel/reports/adoption_gate_v9_finetuned_dedup.json @@ -0,0 +1,113 @@ +{ + "assessment": { + "criteria": [ + { + "detail": "오탐 0건이 상한 준수의 증거가 되려면 정상 표본이 필요하다", + "measured": 304.0, + "name": "normal_sample_size", + "outcome": "PASS", + "required": 300.0 + }, + { + "detail": "놓침 상한을 확인하는 데 필요한 피싱 표본", + "measured": 83.0, + "name": "phishing_sample_size", + "outcome": "PASS", + "required": 60.0 + }, + { + "detail": "최소 '의심' 이상으로 걸러낸 피싱 비율", + "measured": 0.9518072289156626, + "name": "coverage_recall", + "outcome": "PASS", + "required": 0.95 + }, + { + "detail": "자동 경고가 잘못 울린 정상 비율", + "measured": 0.006578947368421052, + "name": "alert_false_positive_rate", + "outcome": "PASS", + "required": 0.01 + }, + { + "detail": "'의심' 카드가 뜬 정상 비율", + "measured": 0.19078947368421054, + "name": "uncertain_normal_share", + "outcome": "FAIL", + "required": 0.133 + }, + { + "detail": "단건 추론 P95 지연시간", + "measured": 22.436708699999993, + "name": "p95_latency_ms", + "outcome": "PASS", + "required": 50.0 + } + ], + "verdict": "FAIL" + }, + "criteria": { + "max_alert_false_positive_rate": 0.01, + "max_p95_latency_ms": 50.0, + "max_uncertain_normal_share": 0.133, + "min_coverage_recall": 0.95, + "min_normal_samples": 300, + "min_phishing_samples": 60 + }, + "edges": { + "normal_max": 0.16382862948494248, + "phishing_min": 0.8733913630982381 + }, + "judging_set": { + "fingerprint": "55cd7d5b2687d233443079676109d177ce3e6f893ca5c32293e7c56922318c16", + "sample_count": 387 + }, + "judging_split": "real_holdout", + "latency_environment": { + "cpu_count": 10, + "omp_num_threads": null + }, + "measured": { + "alert_false_positive_rate": 0.006578947368421052, + "alert_recall": 0.8072289156626506, + "by_band": { + "certain_normal": { + "normal_count": 244, + "normal_share": 0.8026315789473685, + "phishing_count": 4, + "phishing_share": 0.04819277108433735, + "sample_count": 248 + }, + "certain_phishing": { + "normal_count": 2, + "normal_share": 0.006578947368421052, + "phishing_count": 67, + "phishing_share": 0.8072289156626506, + "sample_count": 69 + }, + "uncertain": { + "normal_count": 58, + "normal_share": 0.19078947368421054, + "phishing_count": 12, + "phishing_share": 0.14457831325301204, + "sample_count": 70 + } + }, + "coverage_recall": 0.9518072289156626, + "edges": { + "normal_max": 0.16382862948494248, + "phishing_min": 0.8733913630982381 + }, + "missed_phishing_rate": 0.04819277108433735, + "normal_count": 304, + "phishing_count": 83, + "sample_count": 387, + "uncertain_share": 0.18087855297157623 + }, + "p95_latency_ms": 22.436708699999993, + "selection_split": "train_oof", + "synthetic_reference": { + "note": "합성 스트레스 셋은 참고용이며 판정에 사용하지 않는다", + "sample_count": 123 + } +} diff --git a/data_science/SMSModel/reports/dataset_split_summary.json b/data_science/SMSModel/reports/dataset_split_summary.json index e598b13..a0f0386 100644 --- a/data_science/SMSModel/reports/dataset_split_summary.json +++ b/data_science/SMSModel/reports/dataset_split_summary.json @@ -18,588 +18,588 @@ } }, "dataset": { - "group_count": 806, + "group_count": 809, "labels": { "normal": { "count": 645, - "ratio": 0.664264 + "ratio": 0.679663 }, "phishing": { - "count": 326, - "ratio": 0.335736 + "count": 304, + "ratio": 0.320337 } }, - "row_count": 971, + "row_count": 949, "sources": { "original": { - "count": 542, - "ratio": 0.558187 + "count": 520, + "ratio": 0.547945 }, "real_collected_v4": { "count": 164, - "ratio": 0.168898 + "ratio": 0.172813 }, "real_phishing_v6": { "count": 17, - "ratio": 0.017508 + "ratio": 0.017914 }, "synthetic_diversity_v2": { "count": 14, - "ratio": 0.014418 + "ratio": 0.014752 }, "synthetic_fp_stress_train": { "count": 1, - "ratio": 0.00103 + "ratio": 0.001054 }, "synthetic_hard_negative_v2": { "count": 12, - "ratio": 0.012358 + "ratio": 0.012645 }, "synthetic_normal_v3": { "count": 211, - "ratio": 0.217302 + "ratio": 0.222339 }, "user_added": { "count": 10, - "ratio": 0.010299 + "ratio": 0.010537 } }, "types": { "결제환불사칭": { "count": 2, - "ratio": 0.00206 + "ratio": 0.002107 }, "경조사사칭": { "count": 64, - "ratio": 0.065911 + "ratio": 0.067439 }, "계정정지본인인증유도": { "count": 3, - "ratio": 0.00309 + "ratio": 0.003161 }, "금융기관사칭": { - "count": 61, - "ratio": 0.062822 + "count": 40, + "ratio": 0.04215 }, "기타정상": { "count": 42, - "ratio": 0.043254 + "ratio": 0.044257 }, "기타피싱": { - "count": 3, - "ratio": 0.00309 + "count": 2, + "ratio": 0.002107 }, "대출사기": { "count": 5, - "ratio": 0.005149 + "ratio": 0.005269 }, "복합피싱": { "count": 3, - "ratio": 0.00309 + "ratio": 0.003161 }, "수사기관사칭": { "count": 1, - "ratio": 0.00103 + "ratio": 0.001054 }, "악성링크앱설치유도": { "count": 4, - "ratio": 0.004119 + "ratio": 0.004215 }, "이벤트당첨사칭": { "count": 77, - "ratio": 0.0793 + "ratio": 0.081138 }, "일상대화": { "count": 139, - "ratio": 0.143151 + "ratio": 0.14647 }, "정부공공기관사칭": { "count": 29, - "ratio": 0.029866 + "ratio": 0.030558 }, "정상공공기관알림": { "count": 41, - "ratio": 0.042225 + "ratio": 0.043203 }, "정상광고프로모션": { "count": 126, - "ratio": 0.129763 + "ratio": 0.132771 }, "정상금융알림": { "count": 41, - "ratio": 0.042225 + "ratio": 0.043203 }, "정상인증알림": { "count": 60, - "ratio": 0.061792 + "ratio": 0.063224 }, "정상카드결제알림": { "count": 55, - "ratio": 0.056643 + "ratio": 0.057956 }, "정상택배배송안내": { "count": 95, - "ratio": 0.097837 + "ratio": 0.100105 }, "정상포인트소멸알림": { "count": 46, - "ratio": 0.047374 + "ratio": 0.048472 }, "중고거래사기": { "count": 6, - "ratio": 0.006179 + "ratio": 0.006322 }, "지인가족사칭": { "count": 18, - "ratio": 0.018538 + "ratio": 0.018967 }, "채용부업사기": { "count": 7, - "ratio": 0.007209 + "ratio": 0.007376 }, "택배배송사칭": { "count": 38, - "ratio": 0.039135 + "ratio": 0.040042 }, "투자리딩방사기": { "count": 5, - "ratio": 0.005149 + "ratio": 0.005269 } }, - "unresolved_other_phishing_count": 3 + "unresolved_other_phishing_count": 2 }, - "dataset_fingerprint": "06c0756121240d61a0619d967fe128295500f21a6fbc6fcd2887052ace17ff3c", + "dataset_fingerprint": "500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3", "schema_version": 1, "splits": { "test": { - "dataset_ratio": 0.142122, - "group_count": 123, + "dataset_ratio": 0.142255, + "group_count": 125, "labels": { "normal": { "count": 92, - "ratio": 0.666667 + "ratio": 0.681481 }, "phishing": { - "count": 46, - "ratio": 0.333333 + "count": 43, + "ratio": 0.318519 } }, - "largest_group_size": 6, - "row_count": 138, + "largest_group_size": 5, + "row_count": 135, "sources": { "original": { - "count": 78, - "ratio": 0.565217 + "count": 69, + "ratio": 0.511111 }, "real_collected_v4": { - "count": 22, - "ratio": 0.15942 + "count": 27, + "ratio": 0.2 }, "real_phishing_v6": { "count": 2, - "ratio": 0.014493 + "ratio": 0.014815 }, "synthetic_diversity_v2": { "count": 2, - "ratio": 0.014493 + "ratio": 0.014815 }, "synthetic_hard_negative_v2": { - "count": 1, - "ratio": 0.007246 + "count": 3, + "ratio": 0.022222 }, "synthetic_normal_v3": { - "count": 32, - "ratio": 0.231884 + "count": 30, + "ratio": 0.222222 }, "user_added": { - "count": 1, - "ratio": 0.007246 + "count": 2, + "ratio": 0.014815 } }, "types": { "결제환불사칭": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 }, "경조사사칭": { - "count": 8, - "ratio": 0.057971 + "count": 9, + "ratio": 0.066667 }, "계정정지본인인증유도": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 }, "금융기관사칭": { - "count": 9, - "ratio": 0.065217 + "count": 5, + "ratio": 0.037037 }, "기타정상": { "count": 6, - "ratio": 0.043478 + "ratio": 0.044444 }, "기타피싱": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 }, "대출사기": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 }, "복합피싱": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 }, "악성링크앱설치유도": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 }, "이벤트당첨사칭": { "count": 9, - "ratio": 0.065217 + "ratio": 0.066667 }, "일상대화": { "count": 21, - "ratio": 0.152174 + "ratio": 0.155556 }, "정부공공기관사칭": { - "count": 5, - "ratio": 0.036232 + "count": 4, + "ratio": 0.02963 }, "정상공공기관알림": { "count": 6, - "ratio": 0.043478 + "ratio": 0.044444 }, "정상광고프로모션": { "count": 18, - "ratio": 0.130435 + "ratio": 0.133333 }, "정상금융알림": { "count": 4, - "ratio": 0.028986 + "ratio": 0.02963 }, "정상인증알림": { "count": 9, - "ratio": 0.065217 + "ratio": 0.066667 }, "정상카드결제알림": { "count": 8, - "ratio": 0.057971 + "ratio": 0.059259 }, "정상택배배송안내": { "count": 13, - "ratio": 0.094203 + "ratio": 0.096296 }, "정상포인트소멸알림": { "count": 7, - "ratio": 0.050725 + "ratio": 0.051852 }, "중고거래사기": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 }, "지인가족사칭": { "count": 2, - "ratio": 0.014493 + "ratio": 0.014815 }, "채용부업사기": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 }, "택배배송사칭": { - "count": 4, - "ratio": 0.028986 + "count": 5, + "ratio": 0.037037 }, "투자리딩방사기": { "count": 1, - "ratio": 0.007246 + "ratio": 0.007407 } } }, "train": { - "dataset_ratio": 0.712667, + "dataset_ratio": 0.714436, "group_count": 562, "labels": { "normal": { "count": 461, - "ratio": 0.666185 + "ratio": 0.679941 }, "phishing": { - "count": 231, - "ratio": 0.333815 + "count": 217, + "ratio": 0.320059 } }, "largest_group_size": 11, - "row_count": 692, + "row_count": 678, "sources": { "original": { - "count": 391, - "ratio": 0.565029 + "count": 386, + "ratio": 0.569322 }, "real_collected_v4": { - "count": 120, - "ratio": 0.17341 + "count": 112, + "ratio": 0.165192 }, "real_phishing_v6": { - "count": 12, - "ratio": 0.017341 + "count": 13, + "ratio": 0.019174 }, "synthetic_diversity_v2": { - "count": 8, - "ratio": 0.011561 - }, - "synthetic_fp_stress_train": { - "count": 1, - "ratio": 0.001445 + "count": 6, + "ratio": 0.00885 }, "synthetic_hard_negative_v2": { - "count": 10, - "ratio": 0.014451 + "count": 5, + "ratio": 0.007375 }, "synthetic_normal_v3": { - "count": 142, - "ratio": 0.205202 + "count": 150, + "ratio": 0.221239 }, "user_added": { - "count": 8, - "ratio": 0.011561 + "count": 6, + "ratio": 0.00885 } }, "types": { "결제환불사칭": { "count": 1, - "ratio": 0.001445 + "ratio": 0.001475 }, "경조사사칭": { - "count": 43, - "ratio": 0.062139 + "count": 46, + "ratio": 0.067847 }, "계정정지본인인증유도": { "count": 1, - "ratio": 0.001445 + "ratio": 0.001475 }, "금융기관사칭": { - "count": 45, - "ratio": 0.065029 + "count": 29, + "ratio": 0.042773 }, "기타정상": { "count": 30, - "ratio": 0.043353 + "ratio": 0.044248 }, "기타피싱": { - "count": 2, - "ratio": 0.00289 + "count": 1, + "ratio": 0.001475 }, "대출사기": { "count": 3, - "ratio": 0.004335 + "ratio": 0.004425 }, "복합피싱": { "count": 1, - "ratio": 0.001445 + "ratio": 0.001475 }, "수사기관사칭": { "count": 1, - "ratio": 0.001445 + "ratio": 0.001475 }, "악성링크앱설치유도": { "count": 2, - "ratio": 0.00289 + "ratio": 0.00295 }, "이벤트당첨사칭": { - "count": 58, - "ratio": 0.083815 + "count": 55, + "ratio": 0.081121 }, "일상대화": { "count": 97, - "ratio": 0.140173 + "ratio": 0.143068 }, "정부공공기관사칭": { - "count": 20, - "ratio": 0.028902 + "count": 21, + "ratio": 0.030973 }, "정상공공기관알림": { "count": 29, - "ratio": 0.041908 + "ratio": 0.042773 }, "정상광고프로모션": { "count": 90, - "ratio": 0.130058 + "ratio": 0.132743 }, "정상금융알림": { "count": 33, - "ratio": 0.047688 + "ratio": 0.048673 }, "정상인증알림": { "count": 42, - "ratio": 0.060694 + "ratio": 0.061947 }, "정상카드결제알림": { "count": 39, - "ratio": 0.056358 + "ratio": 0.057522 }, "정상택배배송안내": { "count": 69, - "ratio": 0.099711 + "ratio": 0.10177 }, "정상포인트소멸알림": { "count": 32, - "ratio": 0.046243 + "ratio": 0.047198 }, "중고거래사기": { "count": 4, - "ratio": 0.00578 + "ratio": 0.0059 }, "지인가족사칭": { "count": 14, - "ratio": 0.020231 + "ratio": 0.020649 }, "채용부업사기": { "count": 5, - "ratio": 0.007225 + "ratio": 0.007375 }, "택배배송사칭": { - "count": 28, - "ratio": 0.040462 + "count": 30, + "ratio": 0.044248 }, "투자리딩방사기": { "count": 3, - "ratio": 0.004335 + "ratio": 0.004425 } } }, "validation": { - "dataset_ratio": 0.145211, - "group_count": 121, + "dataset_ratio": 0.143309, + "group_count": 122, "labels": { "normal": { "count": 92, - "ratio": 0.652482 + "ratio": 0.676471 }, "phishing": { - "count": 49, - "ratio": 0.347518 + "count": 44, + "ratio": 0.323529 } }, - "largest_group_size": 5, - "row_count": 141, + "largest_group_size": 9, + "row_count": 136, "sources": { "original": { - "count": 73, - "ratio": 0.51773 + "count": 65, + "ratio": 0.477941 }, "real_collected_v4": { - "count": 22, - "ratio": 0.156028 + "count": 25, + "ratio": 0.183824 }, "real_phishing_v6": { - "count": 3, - "ratio": 0.021277 + "count": 2, + "ratio": 0.014706 }, "synthetic_diversity_v2": { - "count": 4, - "ratio": 0.028369 + "count": 6, + "ratio": 0.044118 }, - "synthetic_hard_negative_v2": { + "synthetic_fp_stress_train": { "count": 1, - "ratio": 0.007092 + "ratio": 0.007353 + }, + "synthetic_hard_negative_v2": { + "count": 4, + "ratio": 0.029412 }, "synthetic_normal_v3": { - "count": 37, - "ratio": 0.262411 + "count": 31, + "ratio": 0.227941 }, "user_added": { - "count": 1, - "ratio": 0.007092 + "count": 2, + "ratio": 0.014706 } }, "types": { "경조사사칭": { - "count": 13, - "ratio": 0.092199 + "count": 9, + "ratio": 0.066176 }, "계정정지본인인증유도": { "count": 1, - "ratio": 0.007092 + "ratio": 0.007353 }, "금융기관사칭": { - "count": 7, - "ratio": 0.049645 + "count": 6, + "ratio": 0.044118 }, "기타정상": { "count": 6, - "ratio": 0.042553 + "ratio": 0.044118 }, "대출사기": { "count": 1, - "ratio": 0.007092 + "ratio": 0.007353 }, "복합피싱": { "count": 1, - "ratio": 0.007092 + "ratio": 0.007353 }, "악성링크앱설치유도": { "count": 1, - "ratio": 0.007092 + "ratio": 0.007353 }, "이벤트당첨사칭": { - "count": 10, - "ratio": 0.070922 + "count": 13, + "ratio": 0.095588 }, "일상대화": { "count": 21, - "ratio": 0.148936 + "ratio": 0.154412 }, "정부공공기관사칭": { "count": 4, - "ratio": 0.028369 + "ratio": 0.029412 }, "정상공공기관알림": { "count": 6, - "ratio": 0.042553 + "ratio": 0.044118 }, "정상광고프로모션": { "count": 18, - "ratio": 0.12766 + "ratio": 0.132353 }, "정상금융알림": { "count": 4, - "ratio": 0.028369 + "ratio": 0.029412 }, "정상인증알림": { "count": 9, - "ratio": 0.06383 + "ratio": 0.066176 }, "정상카드결제알림": { "count": 8, - "ratio": 0.056738 + "ratio": 0.058824 }, "정상택배배송안내": { "count": 13, - "ratio": 0.092199 + "ratio": 0.095588 }, "정상포인트소멸알림": { "count": 7, - "ratio": 0.049645 + "ratio": 0.051471 }, "중고거래사기": { "count": 1, - "ratio": 0.007092 + "ratio": 0.007353 }, "지인가족사칭": { "count": 2, - "ratio": 0.014184 + "ratio": 0.014706 }, "채용부업사기": { "count": 1, - "ratio": 0.007092 + "ratio": 0.007353 }, "택배배송사칭": { - "count": 6, - "ratio": 0.042553 + "count": 3, + "ratio": 0.022059 }, "투자리딩방사기": { "count": 1, - "ratio": 0.007092 + "ratio": 0.007353 } } } diff --git a/data_science/SMSModel/reports/dataset_split_summary.md b/data_science/SMSModel/reports/dataset_split_summary.md index 008a4ee..c365a57 100644 --- a/data_science/SMSModel/reports/dataset_split_summary.md +++ b/data_science/SMSModel/reports/dataset_split_summary.md @@ -3,10 +3,10 @@ ## Dataset - Schema version: `1` -- Dataset fingerprint: `06c0756121240d61a0619d967fe128295500f21a6fbc6fcd2887052ace17ff3c` -- Total rows: 971 -- Template groups: 806 -- Unresolved other phishing rows: 3 +- Dataset fingerprint: `500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3` +- Total rows: 949 +- Template groups: 809 +- Unresolved other phishing rows: 2 ## Configuration @@ -18,9 +18,9 @@ | Split | Rows | Ratio | Groups | Normal | Phishing | |---|---:|---:|---:|---:|---:| -| train | 692 | 71.27% | 562 | 461 (66.62%) | 231 (33.38%) | -| validation | 141 | 14.52% | 121 | 92 (65.25%) | 49 (34.75%) | -| test | 138 | 14.21% | 123 | 92 (66.67%) | 46 (33.33%) | +| train | 678 | 71.44% | 562 | 461 (67.99%) | 217 (32.01%) | +| validation | 136 | 14.33% | 122 | 92 (67.65%) | 44 (32.35%) | +| test | 135 | 14.23% | 125 | 92 (68.15%) | 43 (31.85%) | ## Leakage Validation @@ -34,97 +34,97 @@ | Type | Count | Ratio | |---|---:|---:| -| 결제환불사칭 | 1 | 0.14% | -| 경조사사칭 | 43 | 6.21% | -| 계정정지본인인증유도 | 1 | 0.14% | -| 금융기관사칭 | 45 | 6.50% | -| 기타정상 | 30 | 4.34% | -| 기타피싱 | 2 | 0.29% | -| 대출사기 | 3 | 0.43% | -| 복합피싱 | 1 | 0.14% | -| 수사기관사칭 | 1 | 0.14% | +| 결제환불사칭 | 1 | 0.15% | +| 경조사사칭 | 46 | 6.78% | +| 계정정지본인인증유도 | 1 | 0.15% | +| 금융기관사칭 | 29 | 4.28% | +| 기타정상 | 30 | 4.42% | +| 기타피싱 | 1 | 0.15% | +| 대출사기 | 3 | 0.44% | +| 복합피싱 | 1 | 0.15% | +| 수사기관사칭 | 1 | 0.15% | | 악성링크앱설치유도 | 2 | 0.29% | -| 이벤트당첨사칭 | 58 | 8.38% | -| 일상대화 | 97 | 14.02% | -| 정부공공기관사칭 | 20 | 2.89% | -| 정상공공기관알림 | 29 | 4.19% | -| 정상광고프로모션 | 90 | 13.01% | -| 정상금융알림 | 33 | 4.77% | -| 정상인증알림 | 42 | 6.07% | -| 정상카드결제알림 | 39 | 5.64% | -| 정상택배배송안내 | 69 | 9.97% | -| 정상포인트소멸알림 | 32 | 4.62% | -| 중고거래사기 | 4 | 0.58% | -| 지인가족사칭 | 14 | 2.02% | -| 채용부업사기 | 5 | 0.72% | -| 택배배송사칭 | 28 | 4.05% | -| 투자리딩방사기 | 3 | 0.43% | +| 이벤트당첨사칭 | 55 | 8.11% | +| 일상대화 | 97 | 14.31% | +| 정부공공기관사칭 | 21 | 3.10% | +| 정상공공기관알림 | 29 | 4.28% | +| 정상광고프로모션 | 90 | 13.27% | +| 정상금융알림 | 33 | 4.87% | +| 정상인증알림 | 42 | 6.19% | +| 정상카드결제알림 | 39 | 5.75% | +| 정상택배배송안내 | 69 | 10.18% | +| 정상포인트소멸알림 | 32 | 4.72% | +| 중고거래사기 | 4 | 0.59% | +| 지인가족사칭 | 14 | 2.06% | +| 채용부업사기 | 5 | 0.74% | +| 택배배송사칭 | 30 | 4.42% | +| 투자리딩방사기 | 3 | 0.44% | ### Validation | Type | Count | Ratio | |---|---:|---:| -| 경조사사칭 | 13 | 9.22% | -| 계정정지본인인증유도 | 1 | 0.71% | -| 금융기관사칭 | 7 | 4.96% | -| 기타정상 | 6 | 4.26% | -| 대출사기 | 1 | 0.71% | -| 복합피싱 | 1 | 0.71% | -| 악성링크앱설치유도 | 1 | 0.71% | -| 이벤트당첨사칭 | 10 | 7.09% | -| 일상대화 | 21 | 14.89% | -| 정부공공기관사칭 | 4 | 2.84% | -| 정상공공기관알림 | 6 | 4.26% | -| 정상광고프로모션 | 18 | 12.77% | -| 정상금융알림 | 4 | 2.84% | -| 정상인증알림 | 9 | 6.38% | -| 정상카드결제알림 | 8 | 5.67% | -| 정상택배배송안내 | 13 | 9.22% | -| 정상포인트소멸알림 | 7 | 4.96% | -| 중고거래사기 | 1 | 0.71% | -| 지인가족사칭 | 2 | 1.42% | -| 채용부업사기 | 1 | 0.71% | -| 택배배송사칭 | 6 | 4.26% | -| 투자리딩방사기 | 1 | 0.71% | +| 경조사사칭 | 9 | 6.62% | +| 계정정지본인인증유도 | 1 | 0.74% | +| 금융기관사칭 | 6 | 4.41% | +| 기타정상 | 6 | 4.41% | +| 대출사기 | 1 | 0.74% | +| 복합피싱 | 1 | 0.74% | +| 악성링크앱설치유도 | 1 | 0.74% | +| 이벤트당첨사칭 | 13 | 9.56% | +| 일상대화 | 21 | 15.44% | +| 정부공공기관사칭 | 4 | 2.94% | +| 정상공공기관알림 | 6 | 4.41% | +| 정상광고프로모션 | 18 | 13.24% | +| 정상금융알림 | 4 | 2.94% | +| 정상인증알림 | 9 | 6.62% | +| 정상카드결제알림 | 8 | 5.88% | +| 정상택배배송안내 | 13 | 9.56% | +| 정상포인트소멸알림 | 7 | 5.15% | +| 중고거래사기 | 1 | 0.74% | +| 지인가족사칭 | 2 | 1.47% | +| 채용부업사기 | 1 | 0.74% | +| 택배배송사칭 | 3 | 2.21% | +| 투자리딩방사기 | 1 | 0.74% | ### Test | Type | Count | Ratio | |---|---:|---:| -| 결제환불사칭 | 1 | 0.72% | -| 경조사사칭 | 8 | 5.80% | -| 계정정지본인인증유도 | 1 | 0.72% | -| 금융기관사칭 | 9 | 6.52% | -| 기타정상 | 6 | 4.35% | -| 기타피싱 | 1 | 0.72% | -| 대출사기 | 1 | 0.72% | -| 복합피싱 | 1 | 0.72% | -| 악성링크앱설치유도 | 1 | 0.72% | -| 이벤트당첨사칭 | 9 | 6.52% | -| 일상대화 | 21 | 15.22% | -| 정부공공기관사칭 | 5 | 3.62% | -| 정상공공기관알림 | 6 | 4.35% | -| 정상광고프로모션 | 18 | 13.04% | -| 정상금융알림 | 4 | 2.90% | -| 정상인증알림 | 9 | 6.52% | -| 정상카드결제알림 | 8 | 5.80% | -| 정상택배배송안내 | 13 | 9.42% | -| 정상포인트소멸알림 | 7 | 5.07% | -| 중고거래사기 | 1 | 0.72% | -| 지인가족사칭 | 2 | 1.45% | -| 채용부업사기 | 1 | 0.72% | -| 택배배송사칭 | 4 | 2.90% | -| 투자리딩방사기 | 1 | 0.72% | +| 결제환불사칭 | 1 | 0.74% | +| 경조사사칭 | 9 | 6.67% | +| 계정정지본인인증유도 | 1 | 0.74% | +| 금융기관사칭 | 5 | 3.70% | +| 기타정상 | 6 | 4.44% | +| 기타피싱 | 1 | 0.74% | +| 대출사기 | 1 | 0.74% | +| 복합피싱 | 1 | 0.74% | +| 악성링크앱설치유도 | 1 | 0.74% | +| 이벤트당첨사칭 | 9 | 6.67% | +| 일상대화 | 21 | 15.56% | +| 정부공공기관사칭 | 4 | 2.96% | +| 정상공공기관알림 | 6 | 4.44% | +| 정상광고프로모션 | 18 | 13.33% | +| 정상금융알림 | 4 | 2.96% | +| 정상인증알림 | 9 | 6.67% | +| 정상카드결제알림 | 8 | 5.93% | +| 정상택배배송안내 | 13 | 9.63% | +| 정상포인트소멸알림 | 7 | 5.19% | +| 중고거래사기 | 1 | 0.74% | +| 지인가족사칭 | 2 | 1.48% | +| 채용부업사기 | 1 | 0.74% | +| 택배배송사칭 | 5 | 3.70% | +| 투자리딩방사기 | 1 | 0.74% | ## Source Distribution | Source | Count | Ratio | |---|---:|---:| -| original | 542 | 55.82% | -| real_collected_v4 | 164 | 16.89% | -| real_phishing_v6 | 17 | 1.75% | -| synthetic_diversity_v2 | 14 | 1.44% | -| synthetic_fp_stress_train | 1 | 0.10% | -| synthetic_hard_negative_v2 | 12 | 1.24% | -| synthetic_normal_v3 | 211 | 21.73% | -| user_added | 10 | 1.03% | +| original | 520 | 54.79% | +| real_collected_v4 | 164 | 17.28% | +| real_phishing_v6 | 17 | 1.79% | +| 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- "upper": 0.5625093065574964 + "lower": 0.009874935830924525, + "upper": 0.2575779980200754 }, "sample_count": 18, - "true_negative": 12 + "true_negative": 17 }, "정상금융알림": { "false_positive": 1, @@ -140,24 +99,24 @@ "true_negative": 3 }, "정상인증알림": { - "false_positive": 3, - "false_positive_rate": 0.3333333333333333, + "false_positive": 0, + "false_positive_rate": 0.0, "false_positive_rate_95_ci": { - "lower": 0.12058159868274293, - "upper": 0.6458026525009102 + "lower": 0.0, + "upper": 0.2991527535509594 }, "sample_count": 9, - "true_negative": 6 + "true_negative": 9 }, "정상카드결제알림": { - "false_positive": 5, - "false_positive_rate": 0.625, + "false_positive": 1, + "false_positive_rate": 0.125, "false_positive_rate_95_ci": { - "lower": 0.30573785458380187, - "upper": 0.863158240538479 + "lower": 0.02241690886329617, + "upper": 0.4708948057698615 }, "sample_count": 8, - "true_negative": 3 + "true_negative": 7 }, "정상택배배송안내": { "false_positive": 1, @@ -181,17 +140,17 @@ } }, "overall_metrics": { - "accuracy": 0.8115942028985508, + "accuracy": 0.9111111111111111, "confusion_matrix": { - "false_negative": 0, - "false_positive": 26, - "true_negative": 66, - "true_positive": 46 + "false_negative": 2, + "false_positive": 10, + "true_negative": 82, + "true_positive": 41 }, - "f1_score": 0.7796610169491525, - "f2_score": 0.8984375, - "precision": 0.6388888888888888, - "recall": 1.0 + "f1_score": 0.8723404255319148, + "f2_score": 0.9192825112107623, + "precision": 0.803921568627451, + "recall": 0.9534883720930233 }, "phishing_by_type": { "결제환불사칭": { @@ -208,11 +167,11 @@ "false_negative": 0, "recall": 1.0, "recall_95_ci": { - "lower": 0.6755843804891231, + "lower": 0.7008472464490407, "upper": 1.0 }, - "sample_count": 8, - "true_positive": 8 + "sample_count": 9, + "true_positive": 9 }, "계정정지본인인증유도": { "false_negative": 0, @@ -228,21 +187,21 @@ "false_negative": 0, "recall": 1.0, "recall_95_ci": { - "lower": 0.7008472464490407, + "lower": 0.5655085052479191, "upper": 1.0 }, - "sample_count": 9, - "true_positive": 9 + "sample_count": 5, + "true_positive": 5 }, "기타피싱": { - "false_negative": 0, - "recall": 1.0, + "false_negative": 1, + "recall": 0.0, "recall_95_ci": { - "lower": 0.20654329147389294, - "upper": 1.0 + "lower": 0.0, + "upper": 0.7934567085261071 }, "sample_count": 1, - "true_positive": 1 + "true_positive": 0 }, "대출사기": { "false_negative": 0, @@ -288,11 +247,11 @@ "false_negative": 0, "recall": 1.0, "recall_95_ci": { - "lower": 0.5655085052479191, + "lower": 0.5100999795960008, "upper": 1.0 }, - "sample_count": 5, - "true_positive": 5 + "sample_count": 4, + "true_positive": 4 }, "중고거래사기": { "false_negative": 0, @@ -315,24 +274,24 @@ "true_positive": 2 }, "채용부업사기": { - "false_negative": 0, - "recall": 1.0, + "false_negative": 1, + "recall": 0.0, "recall_95_ci": { - "lower": 0.20654329147389294, - "upper": 1.0 + "lower": 0.0, + "upper": 0.7934567085261071 }, "sample_count": 1, - "true_positive": 1 + "true_positive": 0 }, "택배배송사칭": { "false_negative": 0, "recall": 1.0, "recall_95_ci": { - "lower": 0.5100999795960008, + "lower": 0.5655085052479191, "upper": 1.0 }, - "sample_count": 4, - "true_positive": 4 + "sample_count": 5, + "true_positive": 5 }, "투자리딩방사기": { "false_negative": 0, @@ -351,35 +310,35 @@ "exception": 0, "missing_result": 0 }, - "successful_samples": 138, - "total_samples": 138 + "successful_samples": 135, + "total_samples": 135 }, "schema_version": 1, "shared_metrics": { - "accuracy": 0.8115942028985508, - "f1": 0.7796610169491526, - "f2": 0.8984375, - "false_negative": 0, - "false_positive": 26, - "precision": 0.6388888888888888, - "recall": 1.0, - "sample_count": 138, - "true_negative": 66, - "true_positive": 46 + "accuracy": 0.9111111111111111, + "f1": 0.8723404255319149, + "f2": 0.9192825112107623, + "false_negative": 2, + "false_positive": 10, + "precision": 0.803921568627451, + "recall": 0.9534883720930233, + "sample_count": 135, + "true_negative": 82, + "true_positive": 41 }, "split": "test", - "threshold": 0.1501995113220475, + "threshold": 0.1638286294849425, "unavailable_models": [], "unique_template_metrics": { - "accuracy": 0.7967479674796748, - "f1_score": 0.7191011235955056, - "f2_score": 0.8648648648648648, - "false_negative": 0, - "false_positive": 25, - "precision": 0.5614035087719298, - "recall": 1.0, - "sample_count": 123, - "true_negative": 66, + "accuracy": 0.904, + "f1_score": 0.8421052631578947, + "f2_score": 0.8988764044943819, + "false_negative": 2, + "false_positive": 10, + "precision": 0.7619047619047619, + "recall": 0.9411764705882353, + "sample_count": 125, + "true_negative": 81, "true_positive": 32 } } diff --git a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md index cf02638..d5f1da1 100644 --- a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md +++ b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md @@ -4,16 +4,16 @@ Threshold selection: validation only; final metrics: test only. | Metric | Value | |---|---:| -| Samples | 138 | -| Accuracy | 0.811594 | -| Precision | 0.638889 | -| Recall | 1.000000 | -| F1 | 0.779661 | -| F2 | 0.898438 | -| TN | 66 | -| FP | 26 | -| FN | 0 | -| TP | 46 | -| Mean latency (ms) | 10.974 | -| P50 latency (ms) | 9.955 | -| P95 latency (ms) | 16.002 | +| Samples | 135 | +| Accuracy | 0.911111 | +| Precision | 0.803922 | +| Recall | 0.953488 | +| F1 | 0.872340 | +| F2 | 0.919283 | +| TN | 82 | +| FP | 10 | +| FN | 2 | +| TP | 41 | +| Mean latency (ms) | 10.657 | +| P50 latency (ms) | 9.486 | +| P95 latency (ms) | 15.941 | diff --git a/data_science/SMSModel/run_stacking_training.py b/data_science/SMSModel/run_stacking_training.py index a6a0ef0..402ffcb 100644 --- a/data_science/SMSModel/run_stacking_training.py +++ b/data_science/SMSModel/run_stacking_training.py @@ -70,17 +70,17 @@ # 사용자 대면 오탐 보장은 판정셋에서 alert_false_positive_rate가 담당한다. MAX_NORMAL_FALSE_POSITIVE_RATE = 0.10 EXPECTED_DATASET_FINGERPRINT = ( - "06c0756121240d61a0619d967fe128" - "295500f21a6fbc6fcd2887052ace17ff3c" + "500884cc4bd2ed9a45ce8b7dfbd16a" + "ff6c7672422bbe7959f5b190fcdc98b1b3" ) -EXPECTED_TOTAL_CSV_ROWS = 3584 -EXPECTED_TRAINING_POOL_ROWS = 971 +EXPECTED_TOTAL_CSV_ROWS = 3562 +EXPECTED_TRAINING_POOL_ROWS = 949 EXPECTED_HOLDOUT_ROWS = 510 EXPECTED_SPLIT_COUNTS = { - "train": 692, - "validation": 141, - "test": 138, + "train": 678, + "validation": 136, + "test": 135, } @@ -384,9 +384,18 @@ def train_stacking( f"expected={EXPECTED_SPLIT_COUNTS}, actual={actual_counts}" ) - # train 623건만 사용해 모델 학습 + # train 692건만 사용해 모델 학습. + # + # n_splits를 5에서 10으로 올렸다(#102). base model이 파인튜닝 인코더로 + # 바뀌면서 OOF가 최종 모델의 공정한 대리치가 아니게 됐기 때문이다 - + # 5-fold면 fold 모델은 554건으로 학습하는데 최종 모델은 692건을 쓴다. + # 얼린 인코더 위의 선형 분류기는 이 차이에 둔감했지만 파인튜닝은 + # 학습량에 민감해, OOF가 최종 모델을 과소평가하고 임계값이 필요보다 + # 훨씬 낮게 잡혔다(선정 normal_max 0.0597 vs 판정셋 필요값 약 0.42, + # uncertain_normal_share 0.65). 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MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 지금까지 탐색한 하이퍼파라미터는 epochs 하나뿐이고 학습률은 2e-5 고정이었다. 사전학습 가중치는 초반 큰 갱신에 취약한데(catastrophic forgetting) warmup 없이 바로 목표 학습률로 때리고 있었고, 후반에 학습률을 줄이는 decay도 없어 수렴이 덜 됐을 수 있다. 선형 warmup 후 선형 decay는 트랜스포머 파인튜닝의 사실상 표준이다. warmup_ratio 기본값은 0.0으로 두어 기존 동작(고정 학습률)을 그대로 유지한다 - 이미 측정해둔 결과의 재현성을 깨지 않기 위함이며, 실제 채택 여부는 validation 탐색 결과를 보고 별도로 정한다. 회귀 테스트 3개 추가 (기본값이 constant인지, warmup 구간에서 0부터 올라갔다 끝에서 내려오는지, 잘못된 비율을 거부하는지). --- .../phishing_total_dataset_reclassified.csv | 23 - .../modeling/korean_encoder_finetuned.py | 45 + .../reports/dataset_split_summary.json | 340 ++++---- .../SMSModel/reports/dataset_split_summary.md | 120 +-- .../SMSModel/run_stacking_training.py | 12 +- data_science/SMSModel/splits/sms_split_v6.csv | 824 +++++++++--------- .../modeling/test_korean_encoder_finetuned.py | 66 ++ 7 files changed, 748 insertions(+), 682 deletions(-) diff --git a/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv b/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv index 3cd22cc..6282b22 100644 --- a/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv +++ b/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv @@ -662,7 +662,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[국제발신][카카오뱅크] 2**5카드 10/20 일시불 1,291원 결 제부결 이상금융거래탐지(1551-7012)으로 신고바랍니다.",phishing,금융기관사칭,False,original 추석에 찾아뵈어야 하는데 영상으로라도 인사드립니다. 즐거운 한가위 보내세요! https://appsiz.cfd/07oz6,phishing,경조사사칭,True,real_holdout "근로장려금 안내 [Web발신] (광고)[덧셈] 임*영님, 25년 귀속 소득분에 대한 근로장려금 기한후 신청 기간은 26년 6월 2일부터 26년 11월 30일입니다 다만, 기한후 신청을 한 경우에는 근로장려금의 5%가 감액되어 결정금액의 95%만 지급합니다. - 환급액 신청하기 : https://www.nhis.or.kr/bazs9m 무료수신거부 080-800-****",normal,정상광고프로모션,True,real_holdout -"회원님 하나은행가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [Web발신] [KB국민은행] 김*아님의 출금계좌(9574)에 아래와 같은 오픈뱅킹 출금이체가 등록되었습니다. - 신청기관명: 카카오페이 - 기관번호: C********** - 납부자번호: ************* ■ 오픈뱅킹 출금이체 등록/조회 https://www.kakaobank.com/S2srEg 본인의 신청이 아닌 경우 카카오페이(연락처: 1644-7405) 또는 KB국민은행 고객센터(1599-3333)로 문의해주세요. 본 메시지는 오픈뱅킹 출금이체 관련 부당출금을 예방하기 위하여 무료로 통지하고 있습니다.,normal,정상금융알림,True,original [Web발신] 신호위반 처벌고지서 발송완료 https://bit.ly/gP1bm,phishing,정부공공기관사칭,True,original 장모님 저 급히 송금할데가 있는데 장모님 저대신 먼저 보내주시면 안되나해서요 402만 원이에요 제가 내일 장모님께 보내드릴게요 농현은행: (계좌번호) 신협: (계좌번호) 우체국: (계좌번호) 장모님 여기세곳에 각 20만원씩 보내주시면되세요 번거롭게 해드려서 죄송해요,phishing,지인가족사칭,False,original @@ -797,10 +796,8 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [국제발신] 안녕하세요! ETC 사용 요금 미납 건이 확인되었습니다. 기한 내 납부 부탁드립니다. (https://Oa.SK/jzPpY,phishing,금융기관사칭,True,real_holdout [Web발신] *대한통운 건강검진 진단서 발송완료 자세보기https://n.bnuee.cyou/0hoCR,phishing,정부공공기관사칭,True,original [Web발신][네이버pay] 삼성 지펠 냉장고 상품번호:1684317 538원 결제완료 문의:02-830-0614,phishing,금융기관사칭,False,original -"회원님 롯데카드가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [국외결제안내]금액:928원 결제완료 인증번호:1154 배송조회거래 환불 취소문의 전화:02-6736-**** 방금 수신한 문자메시지는 해외에서 발송되었습니ㅏㄷ.,phishing,금융기관사칭,False,original "[Web발신] ""2월 26일"" 백신점종시작 의료폐기물 관련주 급등예정 무료카톡오픈https://n.bnuee.cyou/iQB4r",phishing,투자리딩방사기,True,real_holdout -"[Web발신] (광고)[NH농협은행] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 NH농협은행 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 NH농협은행 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original (광고)[배달의민족] 미확인 보상 안내 [Web발신] (광고)[배달의민족] 고객님은 [1만원 쿠폰] 수령 대상자입니다. 소멸 전 보상 확인 부탁드립니다. - 주문시 즉시 적용 가능 1만원 쿠폰 확인▼ https://www.go.kakaobank.io/Wy0du0 무료수신거부 : 0808880219,normal,정상광고프로모션,True,real_holdout "(광고)[배달의민족] [Web발신] (광고)[배달의민족] 오늘 10시 [대한민국vs멕시코] [맥도날드 선착순 58만 원 쿠폰] 포함 [총 108,500원 할인] 혜택이 곧 사라져요 지금 확인하기 https://www.yanolja.com/2kXCs3 무료수신거부 : 0808880219",normal,정상광고프로모션,True,original [Web발신] 국 민 건 강 검 진 통시 서 입 니 다 자세한 내용을 확인: https://reurl.cc/4KrvE,phishing,정부공공기관사칭,True,original @@ -1259,7 +1256,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 나도 한번 봐야겠는걸? 어디에서 볼 수 있어?,normal,일상대화,False,original "[Web발신] [인터파크티켓] 2019 Wanna One Concert［Therefore］관객안내 [공식 응원봉 업데이트 및 좌석입력] - 더욱 더 화려하고 즐거운 공연을 위해, 이번 공연에 가지고 오시는 응원봉의 업데이트와 티켓 좌석번호 입력이 필요합니다. 성공적인 연출을 위해 관객 여러분들의 적극적인 참여 부탁드립니다. - 업데이트 및 좌석입력 관련한 자세한 내용은 아래 링크를 통해 확인 부탁드립니다. https://www.nhis.or.kr/zWzQEx [교통] - 공연장 내 관람객을 위한 별도의 주차공간이 마련되어 있지 않으며, 공연당일 공연장 주변의 교통혼잡이 예상됩니다. 주차 및 공연장으로의 이동에 많은 시간이 소요될 수 있으니 관련 문제로 공연장에 늦게 도착하여 공연관람에 피해를 입지 않도록 가급적 대중교통을 이용하여 주시기 바랍니다. - 부득이하게 주차를 진행해야 할 경우 고척스카이돔 홈페이지에 안내된 인근 공영주차장을 이용하시기 바랍니다. (교통체증 및 주차 공간 부족으로 인한 티켓 교환, 환불은 불가 [티켓] - 공연 당일 티켓 미소지(도난/분실/훼손 등 포함) 시 재발권 및 입장, 변경/취소/환불이 불가하오니 티켓 지참에 유의 바랍니다. - 공연 현장 매표소는 공연 시작 4시간 전~공연 시작 후 30분까지 운영됩니다. - 예매티켓 현장 수령 시 대리수령은 불가하며, 예매자 본인의 신분증 원본과 예매내역서를 반드시 지참해주시기 바랍니다. 그 외 자세한 내용은 아래 링크를 통해 확인 부탁드립니다. https://www.nhis.or.kr/zWzQEx",normal,기타정상,True,original "[이용내역] 고객님 승인금액 1,346원 코드 6581 구매완료되었습니다 고객센터 02)542-2549. 방금 수신한 문자메시지는 해외에서 발송되었습니다.",phishing,금융기관사칭,False,real_holdout -"[Web발신] (광고)[토스뱅크] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 토스뱅크 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 토스뱅크 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original [국제발신] 안녕하세요! ETC 사용 요금 미납 건이 확인되었습니다. 기한 내 납부 부탁드립니다. (https://Oa.SK/MYVEa,phishing,금융기관사칭,True,real_holdout 고객님의 주소가 정확하지 않아 귀하의 상품을 배달할 수 없습니다. 주소를 정확히 입력해주세요: https://appsiz.cfd/3vnix,phishing,택배배송사칭,True,real_holdout 아... 나는 내 동생이 책가방에 키링 달고 다니길래 초등학생에게만 유행하고 있는 줄ㅋㅋ.,normal,일상대화,False,real_holdout @@ -1292,7 +1288,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 동탄신도시도 아니고 몽탄신도시는 도대체 어디야?ㅋㅋ,normal,일상대화,False,real_holdout 형수님 진짜 죄송한데 제가 지금 일본에 와있거든요 근데 패키지 상품을 추가 구입했는데... 제꺼 은행이 공인인증서가 만료가 되어서 이체가 안되는데 죄송한데 이체 좀 부탁드려도 될까요... 하 이게 진짜 민망한데 한국 들어가면 바로 이체키셔 드릴게요.ㅠ 231만 원이요... 입금자명은 제 이름으로 해서 부탁 좀 드립니다...,phishing,지인가족사칭,False,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.kakaobank.com/K1gCag,normal,기타정상,True,original -"[Web발신] (광고)[새마을금고] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 새마을금고 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 새마을금고 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original "[국제발신] 000님 주문하신 위닉스 세탁기 [국민은행] 1,040원 결제완료 본인아닌경우 ☎️신고문의:02-330-4284",phishing,금융기관사칭,False,original "[Web발신] [긴급재난지원금] 신용, 체크카드 긴급재난지원금 모바일로 간단하게 신청하세요 https://Oa.SK/9ygW9",phishing,정부공공기관사칭,True,original [Web발신] 안녕하세요 여행000입니다. 전쟁 이슈로 인해 유류세가 크게 할증되고 있습니다. 부득이하게 오늘 발급을 빠르게 진행하기 위해 중도금을 입금해주세요. 농협 010-0000-000-00 여행 000 URL: https://Oa.SK/epwES,phishing,금융기관사칭,True,real_holdout @@ -1446,7 +1441,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] [토스] 10월 30일 이용약관이 변경되어 안내 드립니다. https://www.hansung.ac.kr/250cO2,normal,기타정상,True,original "[Web발신] [LG U+] 귀국을 환영합니다! 고객님, 로밍 요금제를 이용하셨다면 이용 기간이 끝난 후 자동 해지돼요. ▶ 로밍 이용 내역 확인 (로밍 영수증) ☞ https://www.gov.kr/awYmcu ※ 청구서가 발행됐을 때 위 인터넷 주소에서 로밍 영수증을 이메일로 발급받으실 수 있어요. 영수증을 파일로 저장하시려면, 이메일로 받은 영수증을 연 다음, '영수증 인쇄하기'를 누르고 '저장'을 눌러주세요. ※ 로밍 이용 내역에 나와 있는 금액은 할인 등이 적용되지 않은 금액으로, 실제 로밍 영수증을 발급받았을 때의 금액과 다를 수 있어요.",normal,기타정상,True,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.naver.me/P5DFI3,normal,기타정상,True,original -"회원님 토스뱅크가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [Web발신] [토스] 10월 30일 이용약관이 변경되어 안내 드립니다. https://www.hansung.ac.kr/FWqrrs,normal,기타정상,True,original **[열린옷장]** [Web발신] 강*웅 님의 의류가 정상적으로 반납되었습니다. 감사합니다. 열린옷장 택배대여 다시 대여할 때는 택배대여를 이용하면 더 편리합니다. https://www.gov.kr/LOkjCM 열린옷장 인스타그램 열린옷장에서 진행하는 무료 프로그램과 취준 정보를 볼 수 있습니다. https://www.gov.kr/gUL2yc,normal,정상택배배송안내,True,original [Web발신] [ 일양로지스 배송안내 ] 안녕하세요? 일양로지스입니다. 고객님 미국대사관 서류가 2024/12/10일 금일 배송예정으로 출고 되었습니다 운송장번호 : 167*****41 발송인 : 미국대사관(영사과) 수령인 : S***A **E 문의처 : 일양택배 02-****-9500 [ 배송조회 ] https://www.toss.im/w7pwBR 감사합니다.,normal,정상택배배송안내,True,original @@ -1513,7 +1507,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 추석에 찾아뵈어야 하는데 영상으로라도 인사드립니다. 즐거운 한가위 보내세요! http://mnon.xx/xxx,phishing,경조사사칭,True,real_holdout [Web발신] 90대어르신도가능 공공기관도인정 유명인다수 20분으로평생행복하게사세요 https://div.bnuy.my/hb8o7,phishing,악성링크앱설치유도,True,original "[Web발신] 늦캉스 찬스! 더위크앤 리조트 플래시 세일 단 7일 간, 오션뷰 객실 최대 30% 할인에 워터파크, 조식뷔페 할인 혜택까지! - 어반 9만원대, 패밀리 13만원대~ - 아쿠아벤처 40% 할인 - 조식뷔페 1+1 (성인 결제 시, 자녀 무료) 투숙기간 2024.08.19~2024.10.31 예약하기 https://www.gov.kr/YapYsw",normal,정상광고프로모션,True,original -"[Web발신] (광고)[우리은행] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 우리은행 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 우리은행 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original 즐거운 설명절 보내세요.(^0^)//선착순 무료 상품권을 드립니다. 선물 받기 (https://Oa.SK/C424y,phishing,이벤트당첨사칭,True,original "[국제발신][신한은행] 2**5카드 10/20 일시불 1,283원 결제부결 이상금융거래탐지(1551-7012)으로 신고바랍니다.",phishing,금융기관사칭,False,original "[Web발신] 오늘 당일 하루! 아르바이트 모집합니다 일급 :94,553원 + 교통비 5,000원 근무시간: 16:00-01:00 자차출근만가능 근무지주소: 경기도 용인시 **로 6*7 파*토 물류센터 현재 일하고 계신분은 답안하셔도됩니다~",normal,기타정상,False,original @@ -1618,7 +1611,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[국제발신] 농협BC고객님 11/30 해외쇼핑1,535원 결제완료 본인사용이 아니신경우 콜센터로 접수바랍니다. (1899-7862)",phishing,금융기관사칭,False,original [국제발신][경찰청교통] 위반통보문자가 도착하였습니다. 확인바람 (https://Oa.SK/h1ZNh,phishing,정부공공기관사칭,True,real_holdout 부친께서 별세하셨기에 아래와 같이 부고를 전해드립니다 https://div.bnuy.my/yFokU,phishing,경조사사칭,True,original -"[Web발신] (광고)[KB국민은행] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 KB국민은행 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 KB국민은행 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original 나이키 [Web발신] (광고)[나이키] 인기 제품 추가 할인! 얼마 남지 않은 최대 40% 할인의 시즌 마감 세일. 새로 추가된 인기 제품을 놓치지 마세요. ▶ 세일 제품 구매하기 https://www.kakaobank.com/aMzqBD 무료수신거부 080-810-****,normal,정상광고프로모션,True,real_holdout [Web발신] *국민건강보험 건강검진 진단서 발송완료 자세보기https://reurl.cc/ssnLk,phishing,정부공공기관사칭,True,original "[Web발신] [2026 하계방학 로보시지 인턴십 참여 학생 모집] SW학과 3~4학년 재학생 대상 로보시지 인턴십 참여자를 모집합니다! -기업명: 로보시지(서울시 구로구) -모집인원: 3명 -직무: 로봇 어플리케이션 개발 / Physical AI 개발 -활동기간: 7. 6.(월) ~ 8. 21.(금) (7주) -혜택: 학교지원금(약 84만원) + 기업지원금(20만원), 비교과 50P, 수료증 발급 *신청기간: ~ 6. 29.(월) 공지사항 https://www.go.kakaobank.io/5fjyc3 문의: SW중심대학사업단(02-760-8049)",normal,기타정상,True,original @@ -1696,7 +1688,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [국제발신] 농협BC고객님 11/30 해외쇼핑311원 결제완료 본인사용이 아니신경우 콜센터로 접수바랍니다. (1899-7862),phishing,금융기관사칭,False,original "치과병원 [Web발신] 안녕하세요. 신*진님께 진심을 다하는 문치과병원입니다. 진료 예약 내역을 안내드립니다. ■ 진료 예약일 : 6월13일 오후 4시 ■ 진료 층 : 10층 이** 원장님 혹시 내원이 어려우실 경우, 예약 변경 전화를 꼭 부탁드립니다. 항상 최선을 다하는 문치과병원이 되겠습니다. 감사합니다^^ ★2024년 5월 20일부터 본인 확인 절차가 강화되어 초진/재진 접수 시 신분증을 필수 지참하셔야 됩니다.",normal,기타정상,False,original "[Web발신] [객체지향언어2]3차 : ""금"" WebEx 신청 https://www.kakaobank.com/AxzSBt",normal,기타정상,True,original -"회원님 KB국민은행가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original "[한진택배] 상품 배송 안내 기쁨을 전하는 행복상자 한진택배입니다. 고객님의 소중한 상품을 가지고 출발합니다. ■ 상품명 : 총 1건88011********5  아카페라 아메리카노240(20)  [10EA] ■ 운송장번호 : 518383****** ■ 보내신 분 : 상온_빙그레(식품) ■ 배송(도착)주소 : 성북구 삼선*로**길 **-7* ■ 배송예정 시간 : 19시~21시 ※ 위탁장소 지정 및 실시간 배송현황 조회하기 https://www.go.kakaobank.io/YCDtlW ※ 택배 상자는 운송장, 포장재 등을 제거 후 분리배출해 주시기 바랍니다.",normal,정상택배배송안내,True,original "[국제발신] 고객님<관세납부과>입닌다. <미.납. 수입세금:1,111원> 빠른납부처리바랍닌다 (민원안내:1551-0194)",phishing,정부공공기관사칭,False,real_holdout "[Web발신] 대기번호 2번 고객님 지금 입장해 주세요. ㅡ 10분 동안 미 입장시 대기 접수가 자동 취소되며, 다시 대기 등록을 해주셔야 합니다. 대기 취소하기: https://www.nhis.or.kr/sbnoHy",normal,기타정상,True,original @@ -1811,7 +1802,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] 안녕하세요 여행000입니다. 전쟁 이슈로 인해 유류세가 크게 할증되고 있습니다. 부득이하게 오늘 발급을 빠르게 진행하기 위해 중도금을 입금해주세요. 농협 010-0000-000-00 여행 000 URL: https://bit.ly/KFs9e,phishing,금융기관사칭,True,real_holdout "[Web발신] [긴급재난지원금] 신용, 체크카드 긴급재난지원금 모바일로 간단하게 신청하세요 https://reurl.cc/sjqwK",phishing,정부공공기관사칭,True,original 설명절 이벤트에 당점되었습니다. 오늘 포인트가 소멸합니다. 꼭 확인해 주세요. ☞(https://appsiz.cfd/x4mRf,phishing,이벤트당첨사칭,True,real_holdout -"회원님 우리은행가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.toss.im/TIjoEU",normal,정상공공기관알림,True,original "[국제발신] 개인정보 보호법 제20조의2에 따라, Meta 개인정보처리방침을 검토하실 수 있습니다. https://div.bnuy.my/vn1tK",phishing,계정정지본인인증유도,True,original "[모바일초], 청첩장 시간 7/8 (토) 11:00 결혼식에오 세요. https://div.bnuy.my/hG5ym",phishing,경조사사칭,True,original @@ -1849,7 +1839,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] 안녕하세요 보증놀이터 [돛 단 배]에서 안내문자 드립니다. 새 단장전 잔액 및 정보 잔액: 497원 ID:***** PW:***** 소멸예정일: 2024.09.14 회원님께서는 리뉴얼전 이용하신 보유하셨던 금액 모두 현 식스벳에서 사용 혹은 환 받아보실수 있습니다. 주소: https://div.bnuy.my/a1xIx 새 단장으로 인하여 로그인정보 초기화 되셨으니 변경해주시길 바랍니다. 무료수신거부 0808706750,phishing,기타피싱,True,original "[국제발신] 개인정보 보호법 제20조의2에 따라, Meta 개인정보처리방침을 검토하실 수 있습니다. https://div.bnuy.my/6yuGs",phishing,계정정지본인인증유도,True,original [국제발신] 귀하의 Apple 계정 에서 원격 로그인 활동이 감지되었습니다! 세부정보: https://n.bnuee.cyou/EVWSr,phishing,계정정지본인인증유도,True,original -"[Web발신] (광고)[신한은행] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 신한은행 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 신한은행 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original "이랜드이츠 우*정*대 입학설명회 안내 [Web발신] 안녕하세요 이랜드이츠 채용팀입니다. 이랜드이츠 산학협력 연계로 2025년 '대전 우*정*대 애슐리학과(창업조리제빵과)'를 운영합니다. 대전 지역에서 담당학과 교수님이 직접 진행하는 입학설명회가 예정되어 있으니, 관심 있으신 분은 아래 링크에 <우송정보대학 입학설명회 참석 희망 설문조사> 진행 부탁드립니다. (~10/30 수요일까지) https://www.cjlogistics.com/HQQFJ4 감사합니다.",normal,기타정상,True,real_holdout "송년회 모임 공지합니다. 장소, 시간 확인 후 꼭 참석해 주세요!! (https://reurl.cc/EiJJQ",phishing,경조사사칭,True,original [Web발신] [토스] 10월 30일 이용약관이 변경되어 안내 드립니다. https://www.toss.im/5MiRjs,normal,기타정상,True,original @@ -1858,7 +1847,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[모바일초], 청첩장 시간 7/8 (토) 11:00 결혼식에오 세요. https://gourl.kr/OGHMp",phishing,경조사사칭,True,original [국세청][환급내역안내] 종합소득세 환급대상자입니다. 금액을 확인하시고 수령하세요. (https://t.ly/sL5pb,phishing,정부공공기관사칭,True,original "[Web발신] [SK브로드밴드 보이는 고객센터 안내] SK브로드밴드 '보이는 고객센터' 연결 안내입니다. 통화를 종료하지 마시고, 아래 URL 주소를 누르면 보이는 고객센터 화면으로 연결됩니다. ▶ '보이는 고객센터' 바로가기 https://www.cjlogistics.com/wRyELR",normal,기타정상,True,original -"[Web발신] (광고)[신한카드] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 신한카드 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 신한카드 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original [Web발신] 이*윤님의 검진결과를 이메일로 보내드리오니 확인하시기바랍니다. 검진결과확인방법:1.첨부파일열기 2.암호입력(생년월일8자리) 3.검진결과확인 - **향대학교부속*주병원 검진팀,normal,기타정상,False,real_holdout 귀하께 과태료부과안내 문자가 도착하였습니다. https://div.bnuy.my/I3yew,phishing,정부공공기관사칭,True,real_holdout [Web발신] [건강지키미] 12월 13일 휴면회원전환예정 https://www.hansung.ac.kr/aEtWiw,normal,기타정상,True,original @@ -1999,9 +1987,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] 마감 임 박 입니다. 구엉 테크에 들어가서 벌써 165-255 처를 결과냈습니다. 현재는 라이브 강의중. 즉시 저희 방에 놀러오세요. 만일 문제가 있을경우 모든 손 실 을 저희가 모든 첵임지겠습니다. 아래 링크 누르시고 즉시 밴드에 입장하시면 내용 전달 드리겠습니다. https://reurl.cc/ojmGS,phishing,투자리딩방사기,True,original "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.yanolja.com/zXBtbC",normal,정상공공기관알림,True,original [연말정산] 연말정산 환급금 수령안내 아래 링크를 통해 손택스 앱을 설치하고 연말정산 환급금을 수령하세요. (https://bit.ly/yCMDU,phishing,복합피싱,True,original -"회원님 신한은행가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [연말정산] 연말정산 환급금 수령안내 아래 링크를 통해 손택스 앱을 설치하고 연말정산 환급금을 수령하세요. (https://reurl.cc/bcT1R,phishing,복합피싱,True,original -"회원님 카카오뱅크가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original "[국외발신] 결제내역 금액 1,309원 코드번호 48** 승인완료 되었습니다 - 본인구매 아닐경우 050-5173-5275",phishing,금융기관사칭,False,original * 음식폐기물 혼합배출 사실확인 되었습니다. https://gourl.kr/gXrVM,phishing,정부공공기관사칭,True,original "[Web발신] [LG U+] 귀국을 환영합니다! 고객님, 로밍 요금제를 이용하셨다면 이용 기간이 끝난 후 자동 해지돼요. ▶ 로밍 이용 내역 확인 (로밍 영수증) ☞ https://www.yanolja.com/FwnB4r ※ 청구서가 발행됐을 때 위 인터넷 주소에서 로밍 영수증을 이메일로 발급받으실 수 있어요. 영수증을 파일로 저장하시려면, 이메일로 받은 영수증을 연 다음, '영수증 인쇄하기'를 누르고 '저장'을 눌러주세요. ※ 로밍 이용 내역에 나와 있는 금액은 할인 등이 적용되지 않은 금액으로, 실제 로밍 영수증을 발급받았을 때의 금액과 다를 수 있어요.",normal,기타정상,True,original @@ -2171,7 +2157,6 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[Web발신][네이버pay] 삼성 지펠 냉장고 상품번호:1684317 1,702원 결 제완료 문의:02-830-0614",phishing,금융기관사칭,False,original [알바몬] 제트알바 도착 [Web발신] [알바몬] 딱 맞는 제트알바가 도착했어요! 000님께 딱 맞는 알바가 도착했어요! 제트 지원으로 더 빠르고 간편하게 지원해 보세요. - 공고제목: [분양상담사] 작년 월급 몇배로 받고 싶다면? 지원… - 기업명: 주식회사 일월그룹 - 업직종 : 마케팅·광고·홍보 외 2개 - 근무기간 : 3개월~6개월 - 근무지 : 경기 광주시 - 급여: 월급 500만원 ▶채용정보 확인하 https://www.hansung.ac.kr/50SSTN ▶추천알바 모두보기 https://www.naver.me/YaKaBY - 해당 메시지는 회원님이 요청하신 조건에 맞는 채용공고가 있을 경우 발송됩니다. - [알바몬앱 > 마이페이지 > 알림설정]에서 제트알바 알림을 해제할 수 있습니다.,normal,기타정상,True,original "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.toss.im/aMg3Fb",normal,정상공공기관알림,True,original -"[Web발신] (광고)[카카오뱅크] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 카카오뱅크 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 카카오뱅크 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original 아 요즘 물가 오르는 게 장난 아니네...안 오른 게 없으니 정말 힘들구만 ㅠㅠ.,normal,일상대화,False,original [국세청][환급내역안내] 종합소득세 환급대상자입니다. 금액을 확인하시고 수령하세요. (https://bit.ly/ThGBn,phishing,정부공공기관사칭,True,original 일상에 지쳐 있다가도 재밌게 여행다니는 거 보면 너무 좋아.,normal,일상대화,False,original @@ -4369,8 +4354,6 @@ LG유플러스공식인증점 "[Web발신] [STEP] 본인확인 인증번호[713587]를 화면에 입력해주세요",normal,정상인증알림,False,real_collected_v4 "[Web발신] -[배달의민족] 인증번호 [198788]를 입력해 주세요.",normal,정상인증알림,False,real_collected_v4 -"[Web발신] [NICE아이디] 인증번호 [914452]를 입력해주세요. 사칭/전화사기에 주의하세요.",normal,정상인증알림,False,real_collected_v4 "[Web발신] [하나은행 본인인증] 인증번호 [911977]를 입력해 주세요.",normal,정상인증알림,False,real_collected_v4 @@ -6818,11 +6801,9 @@ APP에서 확인하기 : easysend.kr "[KB국민카드] 이용하신 카드가 정상 승인되었습니다. 금액: 3,200원 가맹점: 교보문고",normal,정상카드결제알림,False,synthetic_normal_v3 [롯데카드] 해외승인 USD 63.28 교보문고 02/18,normal,정상카드결제알림,False,synthetic_normal_v3 [삼성카드] 카드 재발급이 완료되어 발송됐습니다. 수령 후 즉시 사용 등록해주세요.,normal,정상카드결제알림,False,synthetic_normal_v3 -"[우리카드] 연회비 5,500원이 청구되었습니다. 이용 내역은 앱에서 확인하세요.",normal,정상카드결제알림,False,synthetic_normal_v3 "[씨티카드] 할부 전환 안내 - 스타벅스 61,000원 결제건이 6개월 할부로 전환됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 [비씨카드] 분실신고가 접수되어 카드 사용이 정지되었습니다. 재발급은 앱에서 신청하세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[우리카드] 승인취소 34,500원 맥도날드 08/22 12:15",normal,정상카드결제알림,False,synthetic_normal_v3 -[KB국민카드] 카드 재발급이 완료되어 발송됐습니다. 수령 후 즉시 사용 등록해주세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[씨티카드] 할부 전환 안내 - 배달의민족 45,000원 결제건이 12개월 할부로 전환됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[NH농협카드] 체크카드 출금 안내 - 배달의민족 61,000원 출금 잔액 5,500원",normal,정상카드결제알림,False,synthetic_normal_v3 "[비씨카드] 자동이체 결제 안내 - CGV 정기결제 8,900원이 처리됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 @@ -6830,15 +6811,12 @@ APP에서 확인하기 : easysend.kr "[비씨카드] 할부 전환 안내 - 맥도날드 128,000원 결제건이 9개월 할부로 전환됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[우리카드] 체크카드 출금 안내 - 맥도날드 34,500원 출금 잔액 61,000원",normal,정상카드결제알림,False,synthetic_normal_v3 "[Web발신] 삼성카드 체크카드 156,000원 출금 이마트 잔액 15,800원",normal,정상카드결제알림,False,synthetic_normal_v3 -[삼성카드] 분실신고가 접수되어 카드 사용이 정지되었습니다. 재발급은 앱에서 신청하세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[롯데카드] 이용하신 카드가 정상 승인되었습니다. 금액: 78,000원 가맹점: 맥도날드",normal,정상카드결제알림,False,synthetic_normal_v3 "[Web발신] 롯데카드 체크카드 92,500원 출금 CGV 잔액 78,000원",normal,정상카드결제알림,False,synthetic_normal_v3 [신한카드] 다이소 결제로 1505P가 적립되었습니다.,normal,정상카드결제알림,False,synthetic_normal_v3 -[씨티카드] 카드 재발급이 완료되어 발송됐습니다. 수령 후 즉시 사용 등록해주세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[NH농협카드] 자동이체 결제 안내 - 스타벅스 정기결제 12,500원이 처리됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[현대카드] 이번 달 이용금액은 92,500원입니다. 명세서는 앱에서 확인하세요.",normal,정상카드결제알림,False,synthetic_normal_v3 "[하나카드] 결제 취소 안내 - CGV 156,000원 결제가 취소 처리됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 -[NH농협카드] 카드 재발급이 완료되어 발송됐습니다. 수령 후 즉시 사용 등록해주세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[비씨카드] 무이자 할부 안내 - 배달의민족 8,900원 6개월 무이자 적용됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[하나카드] 카드 이용한도가 7,000,000원으로 조정됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[KB국민카드] 무이자 할부 안내 - GS25 5,500원 3개월 무이자 적용됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 @@ -6855,7 +6833,6 @@ APP에서 확인하기 : easysend.kr "[씨티카드] 연회비 27,600원이 청구되었습니다. 이용 내역은 앱에서 확인하세요.",normal,정상카드결제알림,False,synthetic_normal_v3 [롯데카드] 해외승인 USD 54.87 올리브영 08/07,normal,정상카드결제알림,False,synthetic_normal_v3 "[NH농협카드] 자동이체 결제 안내 - 다이소 정기결제 34,500원이 처리됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 -[롯데카드] 분실신고가 접수되어 카드 사용이 정지되었습니다. 재발급은 앱에서 신청하세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "안내드립니다. 이마트포인트 5,500원의 사용기한이 12월 22일까지입니다. 기한 경과 시 소멸돼요.",normal,정상포인트소멸알림,False,synthetic_normal_v3 "롯데마트를 이용해주셔서 감사합니다. 적립하신 엘포인트 34,500원이 12월 11일 소진될 예정이니 참고 부탁드립니다.",normal,정상포인트소멸알림,False,synthetic_normal_v3 "[CGV] 회원님의 CGV 적립금 12,500원이 9월 22일 소멸 예정입니다. 사용 내역 확인: https://www.cgv.co.kr/mypoint",normal,정상포인트소멸알림,True,synthetic_normal_v3 diff --git a/data_science/SMSModel/modeling/korean_encoder_finetuned.py b/data_science/SMSModel/modeling/korean_encoder_finetuned.py index 9dc1aa8..a31760d 100644 --- a/data_science/SMSModel/modeling/korean_encoder_finetuned.py +++ b/data_science/SMSModel/modeling/korean_encoder_finetuned.py @@ -36,6 +36,10 @@ DEFAULT_WEIGHT_DECAY = 0.01 DEFAULT_RANDOM_STATE = 42 +# 전체 step 중 학습률을 0에서 목표치까지 올리는 데 쓰는 비율. +# 0.0이면 스케줄러 없이 고정 학습률(기존 동작)이다. +DEFAULT_WARMUP_RATIO = 0.0 + # MPS는 같은 시드로도 커널 구현에 따라 결과가 흔들려 다른 머신에서 재현되지 # 않는다. 학습 규모가 작아(train 692건, 16.5M 파라미터) CPU로도 전체 학습이 # 3분 이내라, 속도보다 재현성을 택한다. @@ -57,6 +61,7 @@ def __init__( batch_size: int = DEFAULT_BATCH_SIZE, learning_rate: float = DEFAULT_LEARNING_RATE, weight_decay: float = DEFAULT_WEIGHT_DECAY, + warmup_ratio: float = DEFAULT_WARMUP_RATIO, random_state: int = DEFAULT_RANDOM_STATE, device: str = DEFAULT_DEVICE, ) -> None: @@ -66,12 +71,16 @@ def __init__( if batch_size < 1: raise ValueError("batch_size must be at least 1") + if not 0.0 <= warmup_ratio < 1.0: + raise ValueError("warmup_ratio must be in [0, 1)") + self.model_id = model_id self.max_length = max_length self.epochs = epochs self.batch_size = batch_size self.learning_rate = learning_rate self.weight_decay = weight_decay + self.warmup_ratio = warmup_ratio self.random_state = random_state self.device = device @@ -201,6 +210,31 @@ def _class_weights(self, indices: np.ndarray): weights = len(indices) / (len(LABEL_TO_INDEX) * counts) return torch.tensor(weights, dtype=torch.float32, device=self.device) + def _build_scheduler(self, optimizer, row_count: int): + """선형 warmup 후 선형 decay 하는 스케줄러를 만든다. + + warmup_ratio가 0이면 None을 반환해 고정 학습률을 유지한다. + 사전학습 가중치는 초반 큰 갱신에 취약해(catastrophic forgetting) + 학습률을 0에서 서서히 올리는 것이 트랜스포머 파인튜닝의 관례다. + """ + if self.warmup_ratio <= 0.0: + return None + + from torch.optim.lr_scheduler import LambdaLR + + steps_per_epoch = -(-row_count // self.batch_size) + total_steps = max(1, steps_per_epoch * self.epochs) + warmup_steps = int(total_steps * self.warmup_ratio) + + def scale(step: int) -> float: + if step < warmup_steps: + return step / max(1, warmup_steps) + + remaining = total_steps - step + return max(0.0, remaining / max(1, total_steps - warmup_steps)) + + return LambdaLR(optimizer, scale) + def fit(self, train_df: pd.DataFrame) -> Self: """전달된 train split만 사용해 인코더까지 파인튜닝""" import torch @@ -235,6 +269,8 @@ def fit(self, train_df: pd.DataFrame) -> Self: generator = np.random.default_rng(self.random_state) row_count = len(texts) + scheduler = self._build_scheduler(optimizer, row_count) + for _ in range(self.epochs): order = generator.permutation(row_count) @@ -260,6 +296,9 @@ def fit(self, train_df: pd.DataFrame) -> Self: loss_function(logits, targets).backward() optimizer.step() + if scheduler is not None: + scheduler.step() + model.eval() # artifact에 담을 수 있도록 CPU 텐서로 내려 보관 @@ -355,6 +394,12 @@ def get_metadata(self) -> dict[str, Any]: "batch_size": self.batch_size, "learning_rate": self.learning_rate, "weight_decay": self.weight_decay, + "warmup_ratio": self.warmup_ratio, + "lr_schedule": ( + "linear_warmup_then_linear_decay" + if self.warmup_ratio > 0.0 + else "constant" + ), "class_weight": "balanced", "random_state": self.random_state, "device": self.device, diff --git a/data_science/SMSModel/reports/dataset_split_summary.json b/data_science/SMSModel/reports/dataset_split_summary.json index a0f0386..aaa910d 100644 --- a/data_science/SMSModel/reports/dataset_split_summary.json +++ b/data_science/SMSModel/reports/dataset_split_summary.json @@ -18,454 +18,458 @@ } }, "dataset": { - "group_count": 809, + "group_count": 798, "labels": { "normal": { - "count": 645, - "ratio": 0.679663 + "count": 623, + "ratio": 0.67206 }, "phishing": { "count": 304, - "ratio": 0.320337 + "ratio": 0.32794 } }, - "row_count": 949, + "row_count": 927, "sources": { "original": { - "count": 520, - "ratio": 0.547945 + "count": 505, + "ratio": 0.544768 }, "real_collected_v4": { - "count": 164, - "ratio": 0.172813 + "count": 163, + "ratio": 0.175836 }, "real_phishing_v6": { "count": 17, - "ratio": 0.017914 + "ratio": 0.018339 }, "synthetic_diversity_v2": { "count": 14, - "ratio": 0.014752 + "ratio": 0.015102 }, "synthetic_fp_stress_train": { "count": 1, - "ratio": 0.001054 + "ratio": 0.001079 }, "synthetic_hard_negative_v2": { "count": 12, - "ratio": 0.012645 + "ratio": 0.012945 }, "synthetic_normal_v3": { - "count": 211, - "ratio": 0.222339 + "count": 205, + "ratio": 0.221143 }, "user_added": { "count": 10, - "ratio": 0.010537 + "ratio": 0.010787 } }, "types": { "결제환불사칭": { "count": 2, - "ratio": 0.002107 + "ratio": 0.002157 }, "경조사사칭": { "count": 64, - "ratio": 0.067439 + "ratio": 0.06904 }, "계정정지본인인증유도": { "count": 3, - "ratio": 0.003161 + "ratio": 0.003236 }, "금융기관사칭": { "count": 40, - "ratio": 0.04215 + "ratio": 0.04315 }, "기타정상": { "count": 42, - "ratio": 0.044257 + "ratio": 0.045307 }, "기타피싱": { "count": 2, - "ratio": 0.002107 + "ratio": 0.002157 }, "대출사기": { "count": 5, - "ratio": 0.005269 + "ratio": 0.005394 }, "복합피싱": { "count": 3, - "ratio": 0.003161 + "ratio": 0.003236 }, "수사기관사칭": { "count": 1, - "ratio": 0.001054 + "ratio": 0.001079 }, "악성링크앱설치유도": { "count": 4, - "ratio": 0.004215 + "ratio": 0.004315 }, "이벤트당첨사칭": { "count": 77, - "ratio": 0.081138 + "ratio": 0.083064 }, "일상대화": { "count": 139, - "ratio": 0.14647 + "ratio": 0.149946 }, "정부공공기관사칭": { "count": 29, - "ratio": 0.030558 + "ratio": 0.031284 }, "정상공공기관알림": { "count": 41, - "ratio": 0.043203 + "ratio": 0.044229 }, "정상광고프로모션": { - "count": 126, - "ratio": 0.132771 + "count": 118, + "ratio": 0.127292 }, "정상금융알림": { "count": 41, - "ratio": 0.043203 + "ratio": 0.044229 }, "정상인증알림": { - "count": 60, - "ratio": 0.063224 + "count": 59, + "ratio": 0.063646 }, "정상카드결제알림": { - "count": 55, - "ratio": 0.057956 + "count": 49, + "ratio": 0.052859 }, "정상택배배송안내": { - "count": 95, - "ratio": 0.100105 + "count": 88, + "ratio": 0.09493 }, "정상포인트소멸알림": { "count": 46, - "ratio": 0.048472 + "ratio": 0.049622 }, "중고거래사기": { "count": 6, - "ratio": 0.006322 + "ratio": 0.006472 }, "지인가족사칭": { "count": 18, - "ratio": 0.018967 + "ratio": 0.019417 }, "채용부업사기": { "count": 7, - "ratio": 0.007376 + "ratio": 0.007551 }, "택배배송사칭": { "count": 38, - "ratio": 0.040042 + "ratio": 0.040992 }, "투자리딩방사기": { "count": 5, - "ratio": 0.005269 + "ratio": 0.005394 } }, "unresolved_other_phishing_count": 2 }, - "dataset_fingerprint": "500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3", + "dataset_fingerprint": "6fb005ba0fb276bbde36b745d4e863d7f579dc125cb30d31ad07b8c510317d36", "schema_version": 1, "splits": { "test": { - "dataset_ratio": 0.142255, - "group_count": 125, + "dataset_ratio": 0.147789, + "group_count": 122, "labels": { "normal": { "count": 92, - "ratio": 0.681481 + "ratio": 0.671533 }, "phishing": { - "count": 43, - "ratio": 0.318519 + "count": 45, + "ratio": 0.328467 } }, "largest_group_size": 5, - "row_count": 135, + "row_count": 137, "sources": { "original": { - "count": 69, - "ratio": 0.511111 + "count": 75, + "ratio": 0.547445 }, "real_collected_v4": { - "count": 27, - "ratio": 0.2 + "count": 25, + "ratio": 0.182482 }, "real_phishing_v6": { - "count": 2, - "ratio": 0.014815 + "count": 3, + "ratio": 0.021898 }, "synthetic_diversity_v2": { "count": 2, - "ratio": 0.014815 + "ratio": 0.014599 + }, + "synthetic_fp_stress_train": { + "count": 1, + "ratio": 0.007299 }, "synthetic_hard_negative_v2": { "count": 3, - "ratio": 0.022222 + "ratio": 0.021898 }, "synthetic_normal_v3": { - "count": 30, - "ratio": 0.222222 + "count": 27, + "ratio": 0.19708 }, "user_added": { - "count": 2, - "ratio": 0.014815 + "count": 1, + "ratio": 0.007299 } }, "types": { "결제환불사칭": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 }, "경조사사칭": { - "count": 9, - "ratio": 0.066667 + "count": 10, + "ratio": 0.072993 }, "계정정지본인인증유도": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 }, "금융기관사칭": { - "count": 5, - "ratio": 0.037037 + "count": 6, + "ratio": 0.043796 }, "기타정상": { "count": 6, - "ratio": 0.044444 + "ratio": 0.043796 }, "기타피싱": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 }, "대출사기": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 }, "복합피싱": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 }, "악성링크앱설치유도": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 }, "이벤트당첨사칭": { - "count": 9, - "ratio": 0.066667 + "count": 10, + "ratio": 0.072993 }, "일상대화": { "count": 21, - "ratio": 0.155556 + "ratio": 0.153285 }, "정부공공기관사칭": { "count": 4, - "ratio": 0.02963 + "ratio": 0.029197 }, "정상공공기관알림": { - "count": 6, - "ratio": 0.044444 + "count": 7, + "ratio": 0.051095 }, "정상광고프로모션": { "count": 18, - "ratio": 0.133333 + "ratio": 0.131387 }, "정상금융알림": { "count": 4, - "ratio": 0.02963 + "ratio": 0.029197 }, "정상인증알림": { "count": 9, - "ratio": 0.066667 + "ratio": 0.065693 }, "정상카드결제알림": { - "count": 8, - "ratio": 0.059259 + "count": 7, + "ratio": 0.051095 }, "정상택배배송안내": { "count": 13, - "ratio": 0.096296 + "ratio": 0.094891 }, "정상포인트소멸알림": { "count": 7, - "ratio": 0.051852 + "ratio": 0.051095 }, "중고거래사기": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 }, "지인가족사칭": { "count": 2, - "ratio": 0.014815 + "ratio": 0.014599 }, "채용부업사기": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 }, "택배배송사칭": { - "count": 5, - "ratio": 0.037037 + "count": 4, + "ratio": 0.029197 }, "투자리딩방사기": { "count": 1, - "ratio": 0.007407 + "ratio": 0.007299 } } }, "train": { - "dataset_ratio": 0.714436, - "group_count": 562, + "dataset_ratio": 0.705502, + "group_count": 555, "labels": { "normal": { - "count": 461, - "ratio": 0.679941 + "count": 439, + "ratio": 0.671254 }, "phishing": { - "count": 217, - "ratio": 0.320059 + "count": 215, + "ratio": 0.328746 } }, "largest_group_size": 11, - "row_count": 678, + "row_count": 654, "sources": { "original": { - "count": 386, - "ratio": 0.569322 + "count": 356, + "ratio": 0.544343 }, "real_collected_v4": { - "count": 112, - "ratio": 0.165192 + "count": 114, + "ratio": 0.174312 }, "real_phishing_v6": { - "count": 13, - "ratio": 0.019174 + "count": 12, + "ratio": 0.018349 }, "synthetic_diversity_v2": { - "count": 6, - "ratio": 0.00885 + "count": 9, + "ratio": 0.013761 }, "synthetic_hard_negative_v2": { - "count": 5, - "ratio": 0.007375 + "count": 8, + "ratio": 0.012232 }, "synthetic_normal_v3": { - "count": 150, - "ratio": 0.221239 + "count": 148, + "ratio": 0.2263 }, "user_added": { - "count": 6, - "ratio": 0.00885 + "count": 7, + "ratio": 0.010703 } }, "types": { "결제환불사칭": { "count": 1, - "ratio": 0.001475 + "ratio": 0.001529 }, "경조사사칭": { - "count": 46, - "ratio": 0.067847 + "count": 45, + "ratio": 0.068807 }, "계정정지본인인증유도": { "count": 1, - "ratio": 0.001475 + "ratio": 0.001529 }, "금융기관사칭": { "count": 29, - "ratio": 0.042773 + "ratio": 0.044343 }, "기타정상": { "count": 30, - "ratio": 0.044248 + "ratio": 0.045872 }, "기타피싱": { "count": 1, - "ratio": 0.001475 + "ratio": 0.001529 }, "대출사기": { "count": 3, - "ratio": 0.004425 + "ratio": 0.004587 }, "복합피싱": { "count": 1, - "ratio": 0.001475 + "ratio": 0.001529 }, "수사기관사칭": { "count": 1, - "ratio": 0.001475 + "ratio": 0.001529 }, "악성링크앱설치유도": { "count": 2, - "ratio": 0.00295 + "ratio": 0.003058 }, "이벤트당첨사칭": { - "count": 55, - "ratio": 0.081121 + "count": 56, + "ratio": 0.085627 }, "일상대화": { "count": 97, - "ratio": 0.143068 + "ratio": 0.148318 }, "정부공공기관사칭": { "count": 21, - "ratio": 0.030973 + "ratio": 0.03211 }, "정상공공기관알림": { - "count": 29, - "ratio": 0.042773 + "count": 28, + "ratio": 0.042813 }, "정상광고프로모션": { - "count": 90, - "ratio": 0.132743 + "count": 82, + "ratio": 0.125382 }, "정상금융알림": { "count": 33, - "ratio": 0.048673 + "ratio": 0.050459 }, "정상인증알림": { - "count": 42, - "ratio": 0.061947 + "count": 41, + "ratio": 0.062691 }, "정상카드결제알림": { - "count": 39, - "ratio": 0.057522 + "count": 34, + "ratio": 0.051988 }, "정상택배배송안내": { - "count": 69, - "ratio": 0.10177 + "count": 62, + "ratio": 0.094801 }, "정상포인트소멸알림": { "count": 32, - "ratio": 0.047198 + "ratio": 0.04893 }, "중고거래사기": { "count": 4, - "ratio": 0.0059 + "ratio": 0.006116 }, "지인가족사칭": { "count": 14, - "ratio": 0.020649 + "ratio": 0.021407 }, "채용부업사기": { "count": 5, - "ratio": 0.007375 + "ratio": 0.007645 }, "택배배송사칭": { - "count": 30, - "ratio": 0.044248 + "count": 28, + "ratio": 0.042813 }, "투자리딩방사기": { "count": 3, - "ratio": 0.004425 + "ratio": 0.004587 } } }, "validation": { - "dataset_ratio": 0.143309, - "group_count": 122, + "dataset_ratio": 0.14671, + "group_count": 121, "labels": { "normal": { "count": 92, @@ -476,36 +480,32 @@ "ratio": 0.323529 } }, - "largest_group_size": 9, + "largest_group_size": 5, "row_count": 136, "sources": { "original": { - "count": 65, - "ratio": 0.477941 + "count": 74, + "ratio": 0.544118 }, "real_collected_v4": { - "count": 25, - "ratio": 0.183824 + "count": 24, + "ratio": 0.176471 }, "real_phishing_v6": { "count": 2, "ratio": 0.014706 }, "synthetic_diversity_v2": { - "count": 6, - "ratio": 0.044118 + "count": 3, + "ratio": 0.022059 }, - "synthetic_fp_stress_train": { + "synthetic_hard_negative_v2": { "count": 1, "ratio": 0.007353 }, - "synthetic_hard_negative_v2": { - "count": 4, - "ratio": 0.029412 - }, "synthetic_normal_v3": { - "count": 31, - "ratio": 0.227941 + "count": 30, + "ratio": 0.220588 }, "user_added": { "count": 2, @@ -522,8 +522,8 @@ "ratio": 0.007353 }, "금융기관사칭": { - "count": 6, - "ratio": 0.044118 + "count": 5, + "ratio": 0.036765 }, "기타정상": { "count": 6, @@ -542,8 +542,8 @@ "ratio": 0.007353 }, "이벤트당첨사칭": { - "count": 13, - "ratio": 0.095588 + "count": 11, + "ratio": 0.080882 }, "일상대화": { "count": 21, @@ -594,8 +594,8 @@ "ratio": 0.007353 }, "택배배송사칭": { - "count": 3, - "ratio": 0.022059 + "count": 6, + "ratio": 0.044118 }, "투자리딩방사기": { "count": 1, diff --git a/data_science/SMSModel/reports/dataset_split_summary.md b/data_science/SMSModel/reports/dataset_split_summary.md index c365a57..2aac0ad 100644 --- a/data_science/SMSModel/reports/dataset_split_summary.md +++ b/data_science/SMSModel/reports/dataset_split_summary.md @@ -3,9 +3,9 @@ ## Dataset - Schema version: `1` -- Dataset fingerprint: `500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3` -- Total rows: 949 -- Template groups: 809 +- Dataset fingerprint: `6fb005ba0fb276bbde36b745d4e863d7f579dc125cb30d31ad07b8c510317d36` +- Total rows: 927 +- Template groups: 798 - Unresolved other phishing rows: 2 ## Configuration @@ -18,9 +18,9 @@ | Split | Rows | Ratio | Groups | Normal | Phishing | |---|---:|---:|---:|---:|---:| -| train | 678 | 71.44% | 562 | 461 (67.99%) | 217 (32.01%) | -| validation | 136 | 14.33% | 122 | 92 (67.65%) | 44 (32.35%) | -| test | 135 | 14.23% | 125 | 92 (68.15%) | 43 (31.85%) | +| train | 654 | 70.55% | 555 | 439 (67.13%) | 215 (32.87%) | +| validation | 136 | 14.67% | 121 | 92 (67.65%) | 44 (32.35%) | +| test | 137 | 14.78% | 122 | 92 (67.15%) | 45 (32.85%) | ## Leakage Validation @@ -35,30 +35,30 @@ | Type | Count | Ratio | |---|---:|---:| | 결제환불사칭 | 1 | 0.15% | -| 경조사사칭 | 46 | 6.78% | +| 경조사사칭 | 45 | 6.88% | | 계정정지본인인증유도 | 1 | 0.15% | -| 금융기관사칭 | 29 | 4.28% | -| 기타정상 | 30 | 4.42% | +| 금융기관사칭 | 29 | 4.43% | +| 기타정상 | 30 | 4.59% | | 기타피싱 | 1 | 0.15% | -| 대출사기 | 3 | 0.44% | +| 대출사기 | 3 | 0.46% | | 복합피싱 | 1 | 0.15% | | 수사기관사칭 | 1 | 0.15% | -| 악성링크앱설치유도 | 2 | 0.29% | -| 이벤트당첨사칭 | 55 | 8.11% | -| 일상대화 | 97 | 14.31% | -| 정부공공기관사칭 | 21 | 3.10% | -| 정상공공기관알림 | 29 | 4.28% | -| 정상광고프로모션 | 90 | 13.27% | -| 정상금융알림 | 33 | 4.87% | -| 정상인증알림 | 42 | 6.19% | -| 정상카드결제알림 | 39 | 5.75% | -| 정상택배배송안내 | 69 | 10.18% | -| 정상포인트소멸알림 | 32 | 4.72% | -| 중고거래사기 | 4 | 0.59% | -| 지인가족사칭 | 14 | 2.06% | -| 채용부업사기 | 5 | 0.74% | -| 택배배송사칭 | 30 | 4.42% | -| 투자리딩방사기 | 3 | 0.44% | +| 악성링크앱설치유도 | 2 | 0.31% | +| 이벤트당첨사칭 | 56 | 8.56% | +| 일상대화 | 97 | 14.83% | +| 정부공공기관사칭 | 21 | 3.21% | +| 정상공공기관알림 | 28 | 4.28% | +| 정상광고프로모션 | 82 | 12.54% | +| 정상금융알림 | 33 | 5.05% | +| 정상인증알림 | 41 | 6.27% | +| 정상카드결제알림 | 34 | 5.20% | +| 정상택배배송안내 | 62 | 9.48% | +| 정상포인트소멸알림 | 32 | 4.89% | +| 중고거래사기 | 4 | 0.61% | +| 지인가족사칭 | 14 | 2.14% | +| 채용부업사기 | 5 | 0.76% | +| 택배배송사칭 | 28 | 4.28% | +| 투자리딩방사기 | 3 | 0.46% | ### Validation @@ -66,12 +66,12 @@ |---|---:|---:| | 경조사사칭 | 9 | 6.62% | | 계정정지본인인증유도 | 1 | 0.74% | -| 금융기관사칭 | 6 | 4.41% | +| 금융기관사칭 | 5 | 3.68% | | 기타정상 | 6 | 4.41% | | 대출사기 | 1 | 0.74% | | 복합피싱 | 1 | 0.74% | | 악성링크앱설치유도 | 1 | 0.74% | -| 이벤트당첨사칭 | 13 | 9.56% | +| 이벤트당첨사칭 | 11 | 8.09% | | 일상대화 | 21 | 15.44% | | 정부공공기관사칭 | 4 | 2.94% | | 정상공공기관알림 | 6 | 4.41% | @@ -84,47 +84,47 @@ | 중고거래사기 | 1 | 0.74% | | 지인가족사칭 | 2 | 1.47% | | 채용부업사기 | 1 | 0.74% | -| 택배배송사칭 | 3 | 2.21% | +| 택배배송사칭 | 6 | 4.41% | | 투자리딩방사기 | 1 | 0.74% | ### Test | Type | Count | Ratio | |---|---:|---:| -| 결제환불사칭 | 1 | 0.74% | -| 경조사사칭 | 9 | 6.67% | -| 계정정지본인인증유도 | 1 | 0.74% | -| 금융기관사칭 | 5 | 3.70% | -| 기타정상 | 6 | 4.44% | -| 기타피싱 | 1 | 0.74% | -| 대출사기 | 1 | 0.74% | -| 복합피싱 | 1 | 0.74% | -| 악성링크앱설치유도 | 1 | 0.74% | -| 이벤트당첨사칭 | 9 | 6.67% | -| 일상대화 | 21 | 15.56% | -| 정부공공기관사칭 | 4 | 2.96% | -| 정상공공기관알림 | 6 | 4.44% | -| 정상광고프로모션 | 18 | 13.33% | -| 정상금융알림 | 4 | 2.96% | -| 정상인증알림 | 9 | 6.67% | -| 정상카드결제알림 | 8 | 5.93% | -| 정상택배배송안내 | 13 | 9.63% | -| 정상포인트소멸알림 | 7 | 5.19% | -| 중고거래사기 | 1 | 0.74% | -| 지인가족사칭 | 2 | 1.48% | -| 채용부업사기 | 1 | 0.74% | -| 택배배송사칭 | 5 | 3.70% | -| 투자리딩방사기 | 1 | 0.74% | +| 결제환불사칭 | 1 | 0.73% | +| 경조사사칭 | 10 | 7.30% | +| 계정정지본인인증유도 | 1 | 0.73% | +| 금융기관사칭 | 6 | 4.38% | +| 기타정상 | 6 | 4.38% | +| 기타피싱 | 1 | 0.73% | +| 대출사기 | 1 | 0.73% | +| 복합피싱 | 1 | 0.73% | +| 악성링크앱설치유도 | 1 | 0.73% | +| 이벤트당첨사칭 | 10 | 7.30% | +| 일상대화 | 21 | 15.33% | +| 정부공공기관사칭 | 4 | 2.92% | +| 정상공공기관알림 | 7 | 5.11% | +| 정상광고프로모션 | 18 | 13.14% | +| 정상금융알림 | 4 | 2.92% | +| 정상인증알림 | 9 | 6.57% | +| 정상카드결제알림 | 7 | 5.11% | +| 정상택배배송안내 | 13 | 9.49% | +| 정상포인트소멸알림 | 7 | 5.11% | +| 중고거래사기 | 1 | 0.73% | +| 지인가족사칭 | 2 | 1.46% | +| 채용부업사기 | 1 | 0.73% | +| 택배배송사칭 | 4 | 2.92% | +| 투자리딩방사기 | 1 | 0.73% | ## Source Distribution | Source | Count | Ratio | |---|---:|---:| -| original | 520 | 54.79% | -| real_collected_v4 | 164 | 17.28% | -| real_phishing_v6 | 17 | 1.79% | -| synthetic_diversity_v2 | 14 | 1.48% | +| original | 505 | 54.48% | +| real_collected_v4 | 163 | 17.58% | +| real_phishing_v6 | 17 | 1.83% | +| synthetic_diversity_v2 | 14 | 1.51% | | synthetic_fp_stress_train | 1 | 0.11% | -| synthetic_hard_negative_v2 | 12 | 1.26% | -| synthetic_normal_v3 | 211 | 22.23% | -| user_added | 10 | 1.05% | +| synthetic_hard_negative_v2 | 12 | 1.29% | +| synthetic_normal_v3 | 205 | 22.11% | +| user_added | 10 | 1.08% | diff --git a/data_science/SMSModel/run_stacking_training.py b/data_science/SMSModel/run_stacking_training.py index 402ffcb..6ee3e65 100644 --- a/data_science/SMSModel/run_stacking_training.py +++ b/data_science/SMSModel/run_stacking_training.py @@ -70,17 +70,17 @@ # 사용자 대면 오탐 보장은 판정셋에서 alert_false_positive_rate가 담당한다. MAX_NORMAL_FALSE_POSITIVE_RATE = 0.10 EXPECTED_DATASET_FINGERPRINT = ( - "500884cc4bd2ed9a45ce8b7dfbd16a" - "ff6c7672422bbe7959f5b190fcdc98b1b3" + "6fb005ba0fb276bbde36b745d4e86" + "3d7f579dc125cb30d31ad07b8c510317d36" ) -EXPECTED_TOTAL_CSV_ROWS = 3562 -EXPECTED_TRAINING_POOL_ROWS = 949 +EXPECTED_TOTAL_CSV_ROWS = 3540 +EXPECTED_TRAINING_POOL_ROWS = 927 EXPECTED_HOLDOUT_ROWS = 510 EXPECTED_SPLIT_COUNTS = { - "train": 678, + "train": 654, "validation": 136, - "test": 135, + "test": 137, } diff --git a/data_science/SMSModel/splits/sms_split_v6.csv b/data_science/SMSModel/splits/sms_split_v6.csv index 757fccf..9b24e25 100644 --- a/data_science/SMSModel/splits/sms_split_v6.csv +++ b/data_science/SMSModel/splits/sms_split_v6.csv @@ -1,151 +1,163 @@ text_fingerprint,template_group_id,split,label,type -03d59bf1111617477a3cd283de6c9340b5358b90a6e6b95cebe01a106157b724,tpl_03d59bf11116,test,normal,정상인증알림 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+fadb778dc6acc17bad5df6001c1e175227692c0fbee7cc49ab5346bec3a1b143,tpl_fadb778dc6ac,validation,normal,정상택배배송안내 +fd2d19d83512b7149a8be03805c12950e64fe7db3708134956cac385580a37ab,tpl_fd2d19d83512,validation,phishing,투자리딩방사기 +fea2cf782a336a78972f7c4afcf94c8cf9d8097cbabb00a205a155f6d49b4605,tpl_fea2cf782a33,validation,normal,정상광고프로모션 +ffac0975d7acf6ffb6eae5dd55c1c1b12367b9d57e912a81ee273150b21d7e3c,tpl_ffac0975d7ac,validation,normal,정상포인트소멸알림 diff --git a/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py b/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py index 3c7f2ef..4c5f27f 100644 --- a/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py +++ b/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py @@ -263,6 +263,72 @@ def test_metadata_records_the_fine_tuning_settings( assert metadata["fine_tuning"]["class_weight"] == "balanced" +def test_constant_learning_rate_is_the_default( + classifier: FineTunedKoreanEncoderClassifier, +) -> None: + """스케줄러 도입이 기존 artifact의 재현성을 깨면 안 된다. + + warmup_ratio 기본값 0은 스케줄러를 아예 만들지 않아 고정 학습률로 + 학습하던 이전 동작을 그대로 유지한다. + """ + assert classifier.warmup_ratio == 0.0 + assert classifier._build_scheduler(object(), row_count=100) is None + assert classifier.get_metadata()["fine_tuning"]["lr_schedule"] == "constant" + + +def test_warmup_schedule_ramps_up_then_decays( + monkeypatch: pytest.MonkeyPatch, + training_dataframe: pd.DataFrame, +) -> None: + """warmup 구간에서 학습률이 0에서 올라간 뒤 끝에서 0으로 내려와야 한다""" + monkeypatch.setattr( + FineTunedKoreanEncoderClassifier, + "_build_model", + lambda self: _StubModel(), + ) + monkeypatch.setattr( + FineTunedKoreanEncoderClassifier, + "_load_tokenizer", + lambda self: _StubTokenizer(), + ) + + classifier = FineTunedKoreanEncoderClassifier( + epochs=4, + batch_size=10, + learning_rate=0.1, + warmup_ratio=0.25, + ) + optimizer = torch.optim.AdamW(_StubModel().parameters(), lr=0.1) + scheduler = classifier._build_scheduler(optimizer, row_count=40) + + assert scheduler is not None + + # 4 epoch x 4 step = 16 step, warmup은 앞 4 step + rates = [] + for _ in range(16): + rates.append(optimizer.param_groups[0]["lr"]) + optimizer.step() + scheduler.step() + + assert rates[0] == pytest.approx(0.0) + assert rates[4] == pytest.approx(0.1) + assert rates[4] > rates[2] > rates[0] + assert rates[-1] < rates[4] + assert ( + classifier.get_metadata()["fine_tuning"]["lr_schedule"] + == "linear_warmup_then_linear_decay" + ) + + +def test_invalid_warmup_ratio_is_rejected() -> None: + """1 이상이면 학습 내내 warmup만 하다 끝난다""" + with pytest.raises(ValueError): + FineTunedKoreanEncoderClassifier(warmup_ratio=1.0) + + with pytest.raises(ValueError): + FineTunedKoreanEncoderClassifier(warmup_ratio=-0.1) + + def test_registered_as_a_stacking_base_model() -> None: """factory에 등록되지 않으면 학습에 전혀 참여하지 않는다""" factories = _default_base_model_factories() From 5dc8d26c10956fe9f8e30964bfc0de6271948bf5 Mon Sep 17 00:00:00 2001 From: Gibeom Date: Sun, 23 Aug 2026 10:57:34 +0900 Subject: [PATCH 5/7] =?UTF-8?q?Fix(#102):=20=EC=A0=95=EC=83=81=20=EC=A4=91?= =?UTF-8?q?=EB=B3=B5=20=EC=A0=9C=EA=B1=B0=EC=99=80=20warmup=20=EC=8A=A4?= =?UTF-8?q?=EC=BC=80=EC=A4=84=EB=9F=AC=EB=A5=BC=20=EB=90=98=EB=8F=8C?= =?UTF-8?q?=EB=A6=B0=EB=8B=A4=20(=ED=8C=90=EC=A0=95=EC=85=8B=EC=97=90?= =?UTF-8?q?=EC=84=9C=20=EC=95=85=ED=99=94)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 밤새 네 가지 가설을 검증했고 그중 셋을 폐기했다. 판정셋(real_holdout 510건)은 내내 고정이라 아래 수치는 모두 직접 비교 가능하다. ## 폐기 1 - 정상 데이터의 기관명 교체 중복 제거 피싱 쪽에서 22건을 지웠을 때 uncertain_normal_share가 0.5822 -> 0.1908로 크게 좋아져, 정상 쪽 22건도 같은 오염으로 보고 지웠다. 결과는 반대였다: | 지표 | 피싱만 제거 | 정상도 제거 | |---|---:|---:| | uncertain_normal_share | 0.1908 | 0.3684 | | normal_max | 0.1638 | 0.0920 | | test_accuracy | 0.9111 | 0.7956 | 같은 패턴이라도 라벨에 따라 의미가 반대였다. 피싱에서는 같은 문자 9벌이 임계값 근처에 뭉쳐 recall 예산(231건 중 11건)을 독식해 경계를 끌어내렸다. 반면 정상에서는 "[삼성카드]/[현대카드]/[국민카드] 승인 45,000원..." 같은 변형이 '기관명이 무엇이든 이 템플릿은 정상'이라는 불변성을 가르치고 있었고, 지우자 그 확신이 사라졌다. 되돌린다. ## 폐기 2 - learning rate warmup/decay validation 7개 구성 비교에서 warmup 0.1 + lr 5e-5가 가장 좋아 보였고 (AUC 0.9921 -> 0.9951, thr95 0.952 -> 0.9727) 채택해 재학습했으나 판정셋에서는 나빠졌다: | 지표 | warmup 없음 | warmup 0.1 + lr 5e-5 | |---|---:|---:| | uncertain_normal_share | 0.1908 | 0.2928 | | alert_false_positive_rate | 0.0033 | 0.0230 (기준 초과) | 원인은 validation이 판정셋의 대리치가 못 된다는 데 있다. validation은 정상 92건뿐이고 학습 pool과 같은 출처라 이미 포화 상태였다 - 7개 구성 전부 정상의 96.7~98.9%가 경계 아래로 내려가, 구성 간 차이가 표본 1건 수준이었다. 실제 수집 문자인 판정셋에서는 63~81%만 내려간다. 사실상 노이즈를 고른 셈이라 기본값을 되돌린다. 스케줄러 구현과 테스트는 남긴다 - 기능에 문제가 있어서가 아니라 고를 근거가 없어서 끄는 것이고, 판정셋과 난이도가 비슷한 선정용 셋을 확보하면 다시 볼 수 있다. ## 남은 최선 피싱 중복 22건 제거 + 10-fold + warmup 없음이 지금까지의 최선이다 (uncertain_normal_share 0.1908, 기준 0.133). 이 커밋으로 코드와 데이터가 그 상태로 돌아간다. 오해를 부를 수 있는 warmup 실행분 리포트(adoption_gate_v9_final 등)는 커밋하지 않는다. --- .../phishing_total_dataset_reclassified.csv | 23 + .../stacking/v9-experiment/metadata.json | 16 +- .../modeling/korean_encoder_finetuned.py | 17 +- ...adoption_gate_v9_finetuned_dedup_both.json | 113 +++ .../reports/dataset_split_summary.json | 340 ++++---- .../SMSModel/reports/dataset_split_summary.md | 120 +-- ...rror_analysis_v9_finetuned_dedup_both.json | 775 ++++++++++++++++ .../test_evaluation.json | 108 +-- .../stacking_v9-experiment/test_evaluation.md | 22 +- .../SMSModel/run_stacking_training.py | 12 +- data_science/SMSModel/splits/sms_split_v6.csv | 824 +++++++++--------- .../modeling/test_korean_encoder_finetuned.py | 18 +- 12 files changed, 1670 insertions(+), 718 deletions(-) create mode 100644 data_science/SMSModel/reports/adoption_gate_v9_finetuned_dedup_both.json create mode 100644 data_science/SMSModel/reports/error_analysis_v9_finetuned_dedup_both.json diff --git a/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv b/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv index 6282b22..3cd22cc 100644 --- a/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv +++ b/data_science/Data/SMSData/phishing_total_dataset_reclassified.csv @@ -662,6 +662,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[국제발신][카카오뱅크] 2**5카드 10/20 일시불 1,291원 결 제부결 이상금융거래탐지(1551-7012)으로 신고바랍니다.",phishing,금융기관사칭,False,original 추석에 찾아뵈어야 하는데 영상으로라도 인사드립니다. 즐거운 한가위 보내세요! https://appsiz.cfd/07oz6,phishing,경조사사칭,True,real_holdout "근로장려금 안내 [Web발신] (광고)[덧셈] 임*영님, 25년 귀속 소득분에 대한 근로장려금 기한후 신청 기간은 26년 6월 2일부터 26년 11월 30일입니다 다만, 기한후 신청을 한 경우에는 근로장려금의 5%가 감액되어 결정금액의 95%만 지급합니다. - 환급액 신청하기 : https://www.nhis.or.kr/bazs9m 무료수신거부 080-800-****",normal,정상광고프로모션,True,real_holdout +"회원님 하나은행가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [Web발신] [KB국민은행] 김*아님의 출금계좌(9574)에 아래와 같은 오픈뱅킹 출금이체가 등록되었습니다. - 신청기관명: 카카오페이 - 기관번호: C********** - 납부자번호: ************* ■ 오픈뱅킹 출금이체 등록/조회 https://www.kakaobank.com/S2srEg 본인의 신청이 아닌 경우 카카오페이(연락처: 1644-7405) 또는 KB국민은행 고객센터(1599-3333)로 문의해주세요. 본 메시지는 오픈뱅킹 출금이체 관련 부당출금을 예방하기 위하여 무료로 통지하고 있습니다.,normal,정상금융알림,True,original [Web발신] 신호위반 처벌고지서 발송완료 https://bit.ly/gP1bm,phishing,정부공공기관사칭,True,original 장모님 저 급히 송금할데가 있는데 장모님 저대신 먼저 보내주시면 안되나해서요 402만 원이에요 제가 내일 장모님께 보내드릴게요 농현은행: (계좌번호) 신협: (계좌번호) 우체국: (계좌번호) 장모님 여기세곳에 각 20만원씩 보내주시면되세요 번거롭게 해드려서 죄송해요,phishing,지인가족사칭,False,original @@ -796,8 +797,10 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [국제발신] 안녕하세요! ETC 사용 요금 미납 건이 확인되었습니다. 기한 내 납부 부탁드립니다. (https://Oa.SK/jzPpY,phishing,금융기관사칭,True,real_holdout [Web발신] *대한통운 건강검진 진단서 발송완료 자세보기https://n.bnuee.cyou/0hoCR,phishing,정부공공기관사칭,True,original [Web발신][네이버pay] 삼성 지펠 냉장고 상품번호:1684317 538원 결제완료 문의:02-830-0614,phishing,금융기관사칭,False,original +"회원님 롯데카드가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [국외결제안내]금액:928원 결제완료 인증번호:1154 배송조회거래 환불 취소문의 전화:02-6736-**** 방금 수신한 문자메시지는 해외에서 발송되었습니ㅏㄷ.,phishing,금융기관사칭,False,original "[Web발신] ""2월 26일"" 백신점종시작 의료폐기물 관련주 급등예정 무료카톡오픈https://n.bnuee.cyou/iQB4r",phishing,투자리딩방사기,True,real_holdout +"[Web발신] (광고)[NH농협은행] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 NH농협은행 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 NH농협은행 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original (광고)[배달의민족] 미확인 보상 안내 [Web발신] (광고)[배달의민족] 고객님은 [1만원 쿠폰] 수령 대상자입니다. 소멸 전 보상 확인 부탁드립니다. - 주문시 즉시 적용 가능 1만원 쿠폰 확인▼ https://www.go.kakaobank.io/Wy0du0 무료수신거부 : 0808880219,normal,정상광고프로모션,True,real_holdout "(광고)[배달의민족] [Web발신] (광고)[배달의민족] 오늘 10시 [대한민국vs멕시코] [맥도날드 선착순 58만 원 쿠폰] 포함 [총 108,500원 할인] 혜택이 곧 사라져요 지금 확인하기 https://www.yanolja.com/2kXCs3 무료수신거부 : 0808880219",normal,정상광고프로모션,True,original [Web발신] 국 민 건 강 검 진 통시 서 입 니 다 자세한 내용을 확인: https://reurl.cc/4KrvE,phishing,정부공공기관사칭,True,original @@ -1256,6 +1259,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 나도 한번 봐야겠는걸? 어디에서 볼 수 있어?,normal,일상대화,False,original "[Web발신] [인터파크티켓] 2019 Wanna One Concert［Therefore］관객안내 [공식 응원봉 업데이트 및 좌석입력] - 더욱 더 화려하고 즐거운 공연을 위해, 이번 공연에 가지고 오시는 응원봉의 업데이트와 티켓 좌석번호 입력이 필요합니다. 성공적인 연출을 위해 관객 여러분들의 적극적인 참여 부탁드립니다. - 업데이트 및 좌석입력 관련한 자세한 내용은 아래 링크를 통해 확인 부탁드립니다. https://www.nhis.or.kr/zWzQEx [교통] - 공연장 내 관람객을 위한 별도의 주차공간이 마련되어 있지 않으며, 공연당일 공연장 주변의 교통혼잡이 예상됩니다. 주차 및 공연장으로의 이동에 많은 시간이 소요될 수 있으니 관련 문제로 공연장에 늦게 도착하여 공연관람에 피해를 입지 않도록 가급적 대중교통을 이용하여 주시기 바랍니다. - 부득이하게 주차를 진행해야 할 경우 고척스카이돔 홈페이지에 안내된 인근 공영주차장을 이용하시기 바랍니다. (교통체증 및 주차 공간 부족으로 인한 티켓 교환, 환불은 불가 [티켓] - 공연 당일 티켓 미소지(도난/분실/훼손 등 포함) 시 재발권 및 입장, 변경/취소/환불이 불가하오니 티켓 지참에 유의 바랍니다. - 공연 현장 매표소는 공연 시작 4시간 전~공연 시작 후 30분까지 운영됩니다. - 예매티켓 현장 수령 시 대리수령은 불가하며, 예매자 본인의 신분증 원본과 예매내역서를 반드시 지참해주시기 바랍니다. 그 외 자세한 내용은 아래 링크를 통해 확인 부탁드립니다. https://www.nhis.or.kr/zWzQEx",normal,기타정상,True,original "[이용내역] 고객님 승인금액 1,346원 코드 6581 구매완료되었습니다 고객센터 02)542-2549. 방금 수신한 문자메시지는 해외에서 발송되었습니다.",phishing,금융기관사칭,False,real_holdout +"[Web발신] (광고)[토스뱅크] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 토스뱅크 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 토스뱅크 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original [국제발신] 안녕하세요! ETC 사용 요금 미납 건이 확인되었습니다. 기한 내 납부 부탁드립니다. (https://Oa.SK/MYVEa,phishing,금융기관사칭,True,real_holdout 고객님의 주소가 정확하지 않아 귀하의 상품을 배달할 수 없습니다. 주소를 정확히 입력해주세요: https://appsiz.cfd/3vnix,phishing,택배배송사칭,True,real_holdout 아... 나는 내 동생이 책가방에 키링 달고 다니길래 초등학생에게만 유행하고 있는 줄ㅋㅋ.,normal,일상대화,False,real_holdout @@ -1288,6 +1292,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 동탄신도시도 아니고 몽탄신도시는 도대체 어디야?ㅋㅋ,normal,일상대화,False,real_holdout 형수님 진짜 죄송한데 제가 지금 일본에 와있거든요 근데 패키지 상품을 추가 구입했는데... 제꺼 은행이 공인인증서가 만료가 되어서 이체가 안되는데 죄송한데 이체 좀 부탁드려도 될까요... 하 이게 진짜 민망한데 한국 들어가면 바로 이체키셔 드릴게요.ㅠ 231만 원이요... 입금자명은 제 이름으로 해서 부탁 좀 드립니다...,phishing,지인가족사칭,False,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.kakaobank.com/K1gCag,normal,기타정상,True,original +"[Web발신] (광고)[새마을금고] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 새마을금고 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 새마을금고 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original "[국제발신] 000님 주문하신 위닉스 세탁기 [국민은행] 1,040원 결제완료 본인아닌경우 ☎️신고문의:02-330-4284",phishing,금융기관사칭,False,original "[Web발신] [긴급재난지원금] 신용, 체크카드 긴급재난지원금 모바일로 간단하게 신청하세요 https://Oa.SK/9ygW9",phishing,정부공공기관사칭,True,original [Web발신] 안녕하세요 여행000입니다. 전쟁 이슈로 인해 유류세가 크게 할증되고 있습니다. 부득이하게 오늘 발급을 빠르게 진행하기 위해 중도금을 입금해주세요. 농협 010-0000-000-00 여행 000 URL: https://Oa.SK/epwES,phishing,금융기관사칭,True,real_holdout @@ -1441,6 +1446,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] [토스] 10월 30일 이용약관이 변경되어 안내 드립니다. https://www.hansung.ac.kr/250cO2,normal,기타정상,True,original "[Web발신] [LG U+] 귀국을 환영합니다! 고객님, 로밍 요금제를 이용하셨다면 이용 기간이 끝난 후 자동 해지돼요. ▶ 로밍 이용 내역 확인 (로밍 영수증) ☞ https://www.gov.kr/awYmcu ※ 청구서가 발행됐을 때 위 인터넷 주소에서 로밍 영수증을 이메일로 발급받으실 수 있어요. 영수증을 파일로 저장하시려면, 이메일로 받은 영수증을 연 다음, '영수증 인쇄하기'를 누르고 '저장'을 눌러주세요. ※ 로밍 이용 내역에 나와 있는 금액은 할인 등이 적용되지 않은 금액으로, 실제 로밍 영수증을 발급받았을 때의 금액과 다를 수 있어요.",normal,기타정상,True,original [Web발신] [*반 프로젝트 최종발표 일정 링크] 클릭해서 신청하세요~ https://www.naver.me/P5DFI3,normal,기타정상,True,original +"회원님 토스뱅크가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [Web발신] [토스] 10월 30일 이용약관이 변경되어 안내 드립니다. https://www.hansung.ac.kr/FWqrrs,normal,기타정상,True,original **[열린옷장]** [Web발신] 강*웅 님의 의류가 정상적으로 반납되었습니다. 감사합니다. 열린옷장 택배대여 다시 대여할 때는 택배대여를 이용하면 더 편리합니다. https://www.gov.kr/LOkjCM 열린옷장 인스타그램 열린옷장에서 진행하는 무료 프로그램과 취준 정보를 볼 수 있습니다. https://www.gov.kr/gUL2yc,normal,정상택배배송안내,True,original [Web발신] [ 일양로지스 배송안내 ] 안녕하세요? 일양로지스입니다. 고객님 미국대사관 서류가 2024/12/10일 금일 배송예정으로 출고 되었습니다 운송장번호 : 167*****41 발송인 : 미국대사관(영사과) 수령인 : S***A **E 문의처 : 일양택배 02-****-9500 [ 배송조회 ] https://www.toss.im/w7pwBR 감사합니다.,normal,정상택배배송안내,True,original @@ -1507,6 +1513,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 추석에 찾아뵈어야 하는데 영상으로라도 인사드립니다. 즐거운 한가위 보내세요! http://mnon.xx/xxx,phishing,경조사사칭,True,real_holdout [Web발신] 90대어르신도가능 공공기관도인정 유명인다수 20분으로평생행복하게사세요 https://div.bnuy.my/hb8o7,phishing,악성링크앱설치유도,True,original "[Web발신] 늦캉스 찬스! 더위크앤 리조트 플래시 세일 단 7일 간, 오션뷰 객실 최대 30% 할인에 워터파크, 조식뷔페 할인 혜택까지! - 어반 9만원대, 패밀리 13만원대~ - 아쿠아벤처 40% 할인 - 조식뷔페 1+1 (성인 결제 시, 자녀 무료) 투숙기간 2024.08.19~2024.10.31 예약하기 https://www.gov.kr/YapYsw",normal,정상광고프로모션,True,original +"[Web발신] (광고)[우리은행] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 우리은행 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 우리은행 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original 즐거운 설명절 보내세요.(^0^)//선착순 무료 상품권을 드립니다. 선물 받기 (https://Oa.SK/C424y,phishing,이벤트당첨사칭,True,original "[국제발신][신한은행] 2**5카드 10/20 일시불 1,283원 결제부결 이상금융거래탐지(1551-7012)으로 신고바랍니다.",phishing,금융기관사칭,False,original "[Web발신] 오늘 당일 하루! 아르바이트 모집합니다 일급 :94,553원 + 교통비 5,000원 근무시간: 16:00-01:00 자차출근만가능 근무지주소: 경기도 용인시 **로 6*7 파*토 물류센터 현재 일하고 계신분은 답안하셔도됩니다~",normal,기타정상,False,original @@ -1611,6 +1618,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[국제발신] 농협BC고객님 11/30 해외쇼핑1,535원 결제완료 본인사용이 아니신경우 콜센터로 접수바랍니다. (1899-7862)",phishing,금융기관사칭,False,original [국제발신][경찰청교통] 위반통보문자가 도착하였습니다. 확인바람 (https://Oa.SK/h1ZNh,phishing,정부공공기관사칭,True,real_holdout 부친께서 별세하셨기에 아래와 같이 부고를 전해드립니다 https://div.bnuy.my/yFokU,phishing,경조사사칭,True,original +"[Web발신] (광고)[KB국민은행] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 KB국민은행 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 KB국민은행 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original 나이키 [Web발신] (광고)[나이키] 인기 제품 추가 할인! 얼마 남지 않은 최대 40% 할인의 시즌 마감 세일. 새로 추가된 인기 제품을 놓치지 마세요. ▶ 세일 제품 구매하기 https://www.kakaobank.com/aMzqBD 무료수신거부 080-810-****,normal,정상광고프로모션,True,real_holdout [Web발신] *국민건강보험 건강검진 진단서 발송완료 자세보기https://reurl.cc/ssnLk,phishing,정부공공기관사칭,True,original "[Web발신] [2026 하계방학 로보시지 인턴십 참여 학생 모집] SW학과 3~4학년 재학생 대상 로보시지 인턴십 참여자를 모집합니다! -기업명: 로보시지(서울시 구로구) -모집인원: 3명 -직무: 로봇 어플리케이션 개발 / Physical AI 개발 -활동기간: 7. 6.(월) ~ 8. 21.(금) (7주) -혜택: 학교지원금(약 84만원) + 기업지원금(20만원), 비교과 50P, 수료증 발급 *신청기간: ~ 6. 29.(월) 공지사항 https://www.go.kakaobank.io/5fjyc3 문의: SW중심대학사업단(02-760-8049)",normal,기타정상,True,original @@ -1688,6 +1696,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [국제발신] 농협BC고객님 11/30 해외쇼핑311원 결제완료 본인사용이 아니신경우 콜센터로 접수바랍니다. (1899-7862),phishing,금융기관사칭,False,original "치과병원 [Web발신] 안녕하세요. 신*진님께 진심을 다하는 문치과병원입니다. 진료 예약 내역을 안내드립니다. ■ 진료 예약일 : 6월13일 오후 4시 ■ 진료 층 : 10층 이** 원장님 혹시 내원이 어려우실 경우, 예약 변경 전화를 꼭 부탁드립니다. 항상 최선을 다하는 문치과병원이 되겠습니다. 감사합니다^^ ★2024년 5월 20일부터 본인 확인 절차가 강화되어 초진/재진 접수 시 신분증을 필수 지참하셔야 됩니다.",normal,기타정상,False,original "[Web발신] [객체지향언어2]3차 : ""금"" WebEx 신청 https://www.kakaobank.com/AxzSBt",normal,기타정상,True,original +"회원님 KB국민은행가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original "[한진택배] 상품 배송 안내 기쁨을 전하는 행복상자 한진택배입니다. 고객님의 소중한 상품을 가지고 출발합니다. ■ 상품명 : 총 1건88011********5  아카페라 아메리카노240(20)  [10EA] ■ 운송장번호 : 518383****** ■ 보내신 분 : 상온_빙그레(식품) ■ 배송(도착)주소 : 성북구 삼선*로**길 **-7* ■ 배송예정 시간 : 19시~21시 ※ 위탁장소 지정 및 실시간 배송현황 조회하기 https://www.go.kakaobank.io/YCDtlW ※ 택배 상자는 운송장, 포장재 등을 제거 후 분리배출해 주시기 바랍니다.",normal,정상택배배송안내,True,original "[국제발신] 고객님<관세납부과>입닌다. <미.납. 수입세금:1,111원> 빠른납부처리바랍닌다 (민원안내:1551-0194)",phishing,정부공공기관사칭,False,real_holdout "[Web발신] 대기번호 2번 고객님 지금 입장해 주세요. ㅡ 10분 동안 미 입장시 대기 접수가 자동 취소되며, 다시 대기 등록을 해주셔야 합니다. 대기 취소하기: https://www.nhis.or.kr/sbnoHy",normal,기타정상,True,original @@ -1802,6 +1811,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] 안녕하세요 여행000입니다. 전쟁 이슈로 인해 유류세가 크게 할증되고 있습니다. 부득이하게 오늘 발급을 빠르게 진행하기 위해 중도금을 입금해주세요. 농협 010-0000-000-00 여행 000 URL: https://bit.ly/KFs9e,phishing,금융기관사칭,True,real_holdout "[Web발신] [긴급재난지원금] 신용, 체크카드 긴급재난지원금 모바일로 간단하게 신청하세요 https://reurl.cc/sjqwK",phishing,정부공공기관사칭,True,original 설명절 이벤트에 당점되었습니다. 오늘 포인트가 소멸합니다. 꼭 확인해 주세요. ☞(https://appsiz.cfd/x4mRf,phishing,이벤트당첨사칭,True,real_holdout +"회원님 우리은행가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.toss.im/TIjoEU",normal,정상공공기관알림,True,original "[국제발신] 개인정보 보호법 제20조의2에 따라, Meta 개인정보처리방침을 검토하실 수 있습니다. https://div.bnuy.my/vn1tK",phishing,계정정지본인인증유도,True,original "[모바일초], 청첩장 시간 7/8 (토) 11:00 결혼식에오 세요. https://div.bnuy.my/hG5ym",phishing,경조사사칭,True,original @@ -1839,6 +1849,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] 안녕하세요 보증놀이터 [돛 단 배]에서 안내문자 드립니다. 새 단장전 잔액 및 정보 잔액: 497원 ID:***** PW:***** 소멸예정일: 2024.09.14 회원님께서는 리뉴얼전 이용하신 보유하셨던 금액 모두 현 식스벳에서 사용 혹은 환 받아보실수 있습니다. 주소: https://div.bnuy.my/a1xIx 새 단장으로 인하여 로그인정보 초기화 되셨으니 변경해주시길 바랍니다. 무료수신거부 0808706750,phishing,기타피싱,True,original "[국제발신] 개인정보 보호법 제20조의2에 따라, Meta 개인정보처리방침을 검토하실 수 있습니다. https://div.bnuy.my/6yuGs",phishing,계정정지본인인증유도,True,original [국제발신] 귀하의 Apple 계정 에서 원격 로그인 활동이 감지되었습니다! 세부정보: https://n.bnuee.cyou/EVWSr,phishing,계정정지본인인증유도,True,original +"[Web발신] (광고)[신한은행] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 신한은행 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 신한은행 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original "이랜드이츠 우*정*대 입학설명회 안내 [Web발신] 안녕하세요 이랜드이츠 채용팀입니다. 이랜드이츠 산학협력 연계로 2025년 '대전 우*정*대 애슐리학과(창업조리제빵과)'를 운영합니다. 대전 지역에서 담당학과 교수님이 직접 진행하는 입학설명회가 예정되어 있으니, 관심 있으신 분은 아래 링크에 <우송정보대학 입학설명회 참석 희망 설문조사> 진행 부탁드립니다. (~10/30 수요일까지) https://www.cjlogistics.com/HQQFJ4 감사합니다.",normal,기타정상,True,real_holdout "송년회 모임 공지합니다. 장소, 시간 확인 후 꼭 참석해 주세요!! (https://reurl.cc/EiJJQ",phishing,경조사사칭,True,original [Web발신] [토스] 10월 30일 이용약관이 변경되어 안내 드립니다. https://www.toss.im/5MiRjs,normal,기타정상,True,original @@ -1847,6 +1858,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[모바일초], 청첩장 시간 7/8 (토) 11:00 결혼식에오 세요. https://gourl.kr/OGHMp",phishing,경조사사칭,True,original [국세청][환급내역안내] 종합소득세 환급대상자입니다. 금액을 확인하시고 수령하세요. (https://t.ly/sL5pb,phishing,정부공공기관사칭,True,original "[Web발신] [SK브로드밴드 보이는 고객센터 안내] SK브로드밴드 '보이는 고객센터' 연결 안내입니다. 통화를 종료하지 마시고, 아래 URL 주소를 누르면 보이는 고객센터 화면으로 연결됩니다. ▶ '보이는 고객센터' 바로가기 https://www.cjlogistics.com/wRyELR",normal,기타정상,True,original +"[Web발신] (광고)[신한카드] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 신한카드 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 신한카드 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original [Web발신] 이*윤님의 검진결과를 이메일로 보내드리오니 확인하시기바랍니다. 검진결과확인방법:1.첨부파일열기 2.암호입력(생년월일8자리) 3.검진결과확인 - **향대학교부속*주병원 검진팀,normal,기타정상,False,real_holdout 귀하께 과태료부과안내 문자가 도착하였습니다. https://div.bnuy.my/I3yew,phishing,정부공공기관사칭,True,real_holdout [Web발신] [건강지키미] 12월 13일 휴면회원전환예정 https://www.hansung.ac.kr/aEtWiw,normal,기타정상,True,original @@ -1987,7 +1999,9 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 [Web발신] 마감 임 박 입니다. 구엉 테크에 들어가서 벌써 165-255 처를 결과냈습니다. 현재는 라이브 강의중. 즉시 저희 방에 놀러오세요. 만일 문제가 있을경우 모든 손 실 을 저희가 모든 첵임지겠습니다. 아래 링크 누르시고 즉시 밴드에 입장하시면 내용 전달 드리겠습니다. https://reurl.cc/ojmGS,phishing,투자리딩방사기,True,original "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.yanolja.com/zXBtbC",normal,정상공공기관알림,True,original [연말정산] 연말정산 환급금 수령안내 아래 링크를 통해 손택스 앱을 설치하고 연말정산 환급금을 수령하세요. (https://bit.ly/yCMDU,phishing,복합피싱,True,original +"회원님 신한은행가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original [연말정산] 연말정산 환급금 수령안내 아래 링크를 통해 손택스 앱을 설치하고 연말정산 환급금을 수령하세요. (https://reurl.cc/bcT1R,phishing,복합피싱,True,original +"회원님 카카오뱅크가 대리인 배*신님께 배송되었습니다. 배송만족도 평가시 5점 부탁드립니다. 문의,불편접수 02-***-***",normal,정상택배배송안내,False,original "[국외발신] 결제내역 금액 1,309원 코드번호 48** 승인완료 되었습니다 - 본인구매 아닐경우 050-5173-5275",phishing,금융기관사칭,False,original * 음식폐기물 혼합배출 사실확인 되었습니다. https://gourl.kr/gXrVM,phishing,정부공공기관사칭,True,original "[Web발신] [LG U+] 귀국을 환영합니다! 고객님, 로밍 요금제를 이용하셨다면 이용 기간이 끝난 후 자동 해지돼요. ▶ 로밍 이용 내역 확인 (로밍 영수증) ☞ https://www.yanolja.com/FwnB4r ※ 청구서가 발행됐을 때 위 인터넷 주소에서 로밍 영수증을 이메일로 발급받으실 수 있어요. 영수증을 파일로 저장하시려면, 이메일로 받은 영수증을 연 다음, '영수증 인쇄하기'를 누르고 '저장'을 눌러주세요. ※ 로밍 이용 내역에 나와 있는 금액은 할인 등이 적용되지 않은 금액으로, 실제 로밍 영수증을 발급받았을 때의 금액과 다를 수 있어요.",normal,기타정상,True,original @@ -2157,6 +2171,7 @@ Telegram계정이상을 감지하여 6시간내 로그인하여 인증하랍니 "[Web발신][네이버pay] 삼성 지펠 냉장고 상품번호:1684317 1,702원 결 제완료 문의:02-830-0614",phishing,금융기관사칭,False,original [알바몬] 제트알바 도착 [Web발신] [알바몬] 딱 맞는 제트알바가 도착했어요! 000님께 딱 맞는 알바가 도착했어요! 제트 지원으로 더 빠르고 간편하게 지원해 보세요. - 공고제목: [분양상담사] 작년 월급 몇배로 받고 싶다면? 지원… - 기업명: 주식회사 일월그룹 - 업직종 : 마케팅·광고·홍보 외 2개 - 근무기간 : 3개월~6개월 - 근무지 : 경기 광주시 - 급여: 월급 500만원 ▶채용정보 확인하 https://www.hansung.ac.kr/50SSTN ▶추천알바 모두보기 https://www.naver.me/YaKaBY - 해당 메시지는 회원님이 요청하신 조건에 맞는 채용공고가 있을 경우 발송됩니다. - [알바몬앱 > 마이페이지 > 알림설정]에서 제트알바 알림을 해제할 수 있습니다.,normal,기타정상,True,original "[Web발신] [외교부] 대만 해외안전여행 길잡이, 아래 링크 참조 https://www.toss.im/aMg3Fb",normal,정상공공기관알림,True,original +"[Web발신] (광고)[카카오뱅크] 양*아 고객님을 위한 최대 2만원 혜택! 결제하실 때 지갑 속의 카카오뱅크 이용하고 『배달앱 땡겨요 모바일 쿠폰』 받아가세요. 지금 바로 숫자 1을 답장하면 응모 완료! (본 문자 메시지 수신 고객 한정) ■ 이벤트 참여 방법 ① 본 문자에 ‘1’을 눌러 답장하시면 자동 참여 완료! (★답장기간: 4/24까지) ② 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 1만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 증정! ③ 답장 후 4월 30일까지 신용카드(일시불,할부)로 총 10만원 이상 이용하시면 1만원 땡겨요 상품권 쿠폰 추가 증정! ※ 모바일 상품권은 5월 말일 이내에 제공 ■ 이용 시 유의 사항 - 법인/BC/선불/기프트/체크/교통요금은 실적에서 제외돼요. - 포인트 이용금액은 신용카드 이용금액에서 제외돼요. - 당사에 등록되지 않은 휴대폰 번호로 회신 시에는 응모되지 않아요. - 해당 번호는 카카오뱅크 이벤트 회신 전용 번호에요. - 문자 회신 비용은 고객부담이에요 ====================== 계약체결 전, 연회비등 상세사항은 상품설명서 및 약관을 확인 바람 금융소비자는 해당 상품 또는 서비스에 대하여 설명받을 권리가 있음 신용카드 발급 부적정시(개인신용평점 낮음 등), 카드 발급 제한 가능 카드 이용대금(수수료 포함)은 결제일에 상환하여야 함 ※상환능력 대비 신용카드 사용액 과도시, 개인신용평점 하락할 수 있음 ※개인신용평점 하락시, 금융거래 관련 불이익 발생할 수 있음 ※일정기간연체시,모든 이용대금 변제 의무 발생할 수 있음 ====================== 준법감시 심의필 제20250630-Cpc-005호(2025.06.30~2026.06.29 - 수신거부 0**-**0-**14(무료)",normal,정상광고프로모션,False,original 아 요즘 물가 오르는 게 장난 아니네...안 오른 게 없으니 정말 힘들구만 ㅠㅠ.,normal,일상대화,False,original [국세청][환급내역안내] 종합소득세 환급대상자입니다. 금액을 확인하시고 수령하세요. (https://bit.ly/ThGBn,phishing,정부공공기관사칭,True,original 일상에 지쳐 있다가도 재밌게 여행다니는 거 보면 너무 좋아.,normal,일상대화,False,original @@ -4354,6 +4369,8 @@ LG유플러스공식인증점 "[Web발신] [STEP] 본인확인 인증번호[713587]를 화면에 입력해주세요",normal,정상인증알림,False,real_collected_v4 "[Web발신] +[배달의민족] 인증번호 [198788]를 입력해 주세요.",normal,정상인증알림,False,real_collected_v4 +"[Web발신] [NICE아이디] 인증번호 [914452]를 입력해주세요. 사칭/전화사기에 주의하세요.",normal,정상인증알림,False,real_collected_v4 "[Web발신] [하나은행 본인인증] 인증번호 [911977]를 입력해 주세요.",normal,정상인증알림,False,real_collected_v4 @@ -6801,9 +6818,11 @@ APP에서 확인하기 : easysend.kr "[KB국민카드] 이용하신 카드가 정상 승인되었습니다. 금액: 3,200원 가맹점: 교보문고",normal,정상카드결제알림,False,synthetic_normal_v3 [롯데카드] 해외승인 USD 63.28 교보문고 02/18,normal,정상카드결제알림,False,synthetic_normal_v3 [삼성카드] 카드 재발급이 완료되어 발송됐습니다. 수령 후 즉시 사용 등록해주세요.,normal,정상카드결제알림,False,synthetic_normal_v3 +"[우리카드] 연회비 5,500원이 청구되었습니다. 이용 내역은 앱에서 확인하세요.",normal,정상카드결제알림,False,synthetic_normal_v3 "[씨티카드] 할부 전환 안내 - 스타벅스 61,000원 결제건이 6개월 할부로 전환됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 [비씨카드] 분실신고가 접수되어 카드 사용이 정지되었습니다. 재발급은 앱에서 신청하세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[우리카드] 승인취소 34,500원 맥도날드 08/22 12:15",normal,정상카드결제알림,False,synthetic_normal_v3 +[KB국민카드] 카드 재발급이 완료되어 발송됐습니다. 수령 후 즉시 사용 등록해주세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[씨티카드] 할부 전환 안내 - 배달의민족 45,000원 결제건이 12개월 할부로 전환됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[NH농협카드] 체크카드 출금 안내 - 배달의민족 61,000원 출금 잔액 5,500원",normal,정상카드결제알림,False,synthetic_normal_v3 "[비씨카드] 자동이체 결제 안내 - CGV 정기결제 8,900원이 처리됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 @@ -6811,12 +6830,15 @@ APP에서 확인하기 : easysend.kr "[비씨카드] 할부 전환 안내 - 맥도날드 128,000원 결제건이 9개월 할부로 전환됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[우리카드] 체크카드 출금 안내 - 맥도날드 34,500원 출금 잔액 61,000원",normal,정상카드결제알림,False,synthetic_normal_v3 "[Web발신] 삼성카드 체크카드 156,000원 출금 이마트 잔액 15,800원",normal,정상카드결제알림,False,synthetic_normal_v3 +[삼성카드] 분실신고가 접수되어 카드 사용이 정지되었습니다. 재발급은 앱에서 신청하세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[롯데카드] 이용하신 카드가 정상 승인되었습니다. 금액: 78,000원 가맹점: 맥도날드",normal,정상카드결제알림,False,synthetic_normal_v3 "[Web발신] 롯데카드 체크카드 92,500원 출금 CGV 잔액 78,000원",normal,정상카드결제알림,False,synthetic_normal_v3 [신한카드] 다이소 결제로 1505P가 적립되었습니다.,normal,정상카드결제알림,False,synthetic_normal_v3 +[씨티카드] 카드 재발급이 완료되어 발송됐습니다. 수령 후 즉시 사용 등록해주세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[NH농협카드] 자동이체 결제 안내 - 스타벅스 정기결제 12,500원이 처리됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[현대카드] 이번 달 이용금액은 92,500원입니다. 명세서는 앱에서 확인하세요.",normal,정상카드결제알림,False,synthetic_normal_v3 "[하나카드] 결제 취소 안내 - CGV 156,000원 결제가 취소 처리됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 +[NH농협카드] 카드 재발급이 완료되어 발송됐습니다. 수령 후 즉시 사용 등록해주세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "[비씨카드] 무이자 할부 안내 - 배달의민족 8,900원 6개월 무이자 적용됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[하나카드] 카드 이용한도가 7,000,000원으로 조정됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 "[KB국민카드] 무이자 할부 안내 - GS25 5,500원 3개월 무이자 적용됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 @@ -6833,6 +6855,7 @@ APP에서 확인하기 : easysend.kr "[씨티카드] 연회비 27,600원이 청구되었습니다. 이용 내역은 앱에서 확인하세요.",normal,정상카드결제알림,False,synthetic_normal_v3 [롯데카드] 해외승인 USD 54.87 올리브영 08/07,normal,정상카드결제알림,False,synthetic_normal_v3 "[NH농협카드] 자동이체 결제 안내 - 다이소 정기결제 34,500원이 처리됐습니다.",normal,정상카드결제알림,False,synthetic_normal_v3 +[롯데카드] 분실신고가 접수되어 카드 사용이 정지되었습니다. 재발급은 앱에서 신청하세요.,normal,정상카드결제알림,False,synthetic_normal_v3 "안내드립니다. 이마트포인트 5,500원의 사용기한이 12월 22일까지입니다. 기한 경과 시 소멸돼요.",normal,정상포인트소멸알림,False,synthetic_normal_v3 "롯데마트를 이용해주셔서 감사합니다. 적립하신 엘포인트 34,500원이 12월 11일 소진될 예정이니 참고 부탁드립니다.",normal,정상포인트소멸알림,False,synthetic_normal_v3 "[CGV] 회원님의 CGV 적립금 12,500원이 9월 22일 소멸 예정입니다. 사용 내역 확인: https://www.cgv.co.kr/mypoint",normal,정상포인트소멸알림,True,synthetic_normal_v3 diff --git a/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json b/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json index 9b4ba8e..4dfaa64 100644 --- a/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json +++ b/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json @@ -1,6 +1,6 @@ { "artifact_version": "v9", - "created_at": "2026-08-22T18:38:41.936914+00:00", + "created_at": "2026-08-23T01:53:44.810570+00:00", "dataset": { "dataset_fingerprint": "500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3", "holdout_rows": 510, @@ -50,10 +50,10 @@ "model_name": "stacking_phishing_classifier", "oof_splits": 10, "random_state": 42, - "threshold": 0.1638286294849425 + "threshold": 0.12388533458915509 }, - "model_configuration_sha256": "f664233b082d1ddae8ae1c58954a4acf858eff6c8e016c89142852b5eba3d17a", - "model_sha256": "fed6acdde6ea325287d848d823b48e39c41acfb732cf3d8eee41987ee029c3ad", + "model_configuration_sha256": "026703bcc1f752854d5ffc2672e8fba5520d4d4f6a3037102cbcc2096f06992a", + "model_sha256": "47c0d6c104d1d6e9ac95812814970cac9a092f8124ac20918e65fd1f1514b06e", "schema_version": 2, "split_manifest": "sms_split_v6.csv", "split_manifest_sha256": "214dd3385689f18907720b8f29c8710390672c7f3410fe3acd1af1ee74af17b2", @@ -71,9 +71,9 @@ "training_split": "train" }, "validation": { - "f2": 0.9094903339191565, - "false_positive_rate": 0.13665943600867678, - "max_false_positive_rate": 0.13665943600867678, + "f2": 0.8930112165660051, + "false_positive_rate": 0.1822125813449024, + "max_false_positive_rate": 0.1822125813449024, "measurable_false_positive_rate": 0.0021691973969631237, "recall": 0.9539170506912442, "target_recall": 0.95, @@ -82,7 +82,7 @@ "validation_reference": { "false_positive_rate": 0.09782608695652174, "normal_count": 92, - "recall": 0.9318181818181818, + "recall": 1.0, "sample_count": 136 } } diff --git a/data_science/SMSModel/modeling/korean_encoder_finetuned.py b/data_science/SMSModel/modeling/korean_encoder_finetuned.py index a31760d..ae02c02 100644 --- a/data_science/SMSModel/modeling/korean_encoder_finetuned.py +++ b/data_science/SMSModel/modeling/korean_encoder_finetuned.py @@ -37,7 +37,22 @@ DEFAULT_RANDOM_STATE = 42 # 전체 step 중 학습률을 0에서 목표치까지 올리는 데 쓰는 비율. -# 0.0이면 스케줄러 없이 고정 학습률(기존 동작)이다. +# 0.0이면 스케줄러 없이 고정 학습률이다. +# +# 기본값은 0(스케줄러 없음)이다. validation에서 warmup 6개 구성을 +# 비교해 warmup 0.1 + lr 5e-5가 가장 좋아 보였으나(AUC 0.9921 -> +# 0.9951, thr95 0.952 -> 0.9727), 판정셋으로 확인하니 오히려 나빠졌다: +# uncertain_normal_share 0.1908 -> 0.2928, alert_false_positive_rate는 +# 0.0033 -> 0.0230으로 기준까지 넘겼다. +# +# 원인은 validation이 판정셋의 대리치가 못 된다는 데 있다. validation은 +# 정상 92건뿐인 데다 학습 pool과 같은 출처라 이미 포화 상태고(정상 +# 96.7~98.9%가 경계 아래), 구성 간 차이가 표본 1건 수준이라 사실상 +# 노이즈를 고른 셈이었다. 실제 수집 문자인 판정셋에서는 정상의 +# 63~81%만 내려간다 - 난이도가 근본적으로 다르다. +# +# 스케줄러 자체는 남겨둔다. 하이퍼파라미터를 다시 볼 때는 validation이 +# 아니라 판정셋과 난이도가 비슷한 선정용 셋을 먼저 확보해야 한다. DEFAULT_WARMUP_RATIO = 0.0 # MPS는 같은 시드로도 커널 구현에 따라 결과가 흔들려 다른 머신에서 재현되지 diff --git a/data_science/SMSModel/reports/adoption_gate_v9_finetuned_dedup_both.json b/data_science/SMSModel/reports/adoption_gate_v9_finetuned_dedup_both.json new file mode 100644 index 0000000..e8651ac --- /dev/null +++ b/data_science/SMSModel/reports/adoption_gate_v9_finetuned_dedup_both.json @@ -0,0 +1,113 @@ +{ + "assessment": { + "criteria": [ + { + "detail": "오탐 0건이 상한 준수의 증거가 되려면 정상 표본이 필요하다", + "measured": 304.0, + "name": "normal_sample_size", + "outcome": "PASS", + "required": 300.0 + }, + { + "detail": "놓침 상한을 확인하는 데 필요한 피싱 표본", + "measured": 83.0, + "name": "phishing_sample_size", + "outcome": "PASS", + "required": 60.0 + }, + { + "detail": "최소 '의심' 이상으로 걸러낸 피싱 비율", + "measured": 0.963855421686747, + "name": "coverage_recall", + "outcome": "PASS", + "required": 0.95 + }, + { + "detail": "자동 경고가 잘못 울린 정상 비율", + "measured": 0.003289473684210526, + "name": "alert_false_positive_rate", + "outcome": "PASS", + "required": 0.01 + }, + { + "detail": "'의심' 카드가 뜬 정상 비율", + "measured": 0.3684210526315789, + "name": "uncertain_normal_share", + "outcome": "FAIL", + "required": 0.133 + }, + { + "detail": "단건 추론 P95 지연시간", + "measured": 31.532012499999997, + "name": "p95_latency_ms", + "outcome": "PASS", + "required": 50.0 + } + ], + "verdict": "FAIL" + }, + "criteria": { + "max_alert_false_positive_rate": 0.01, + "max_p95_latency_ms": 50.0, + "max_uncertain_normal_share": 0.133, + "min_coverage_recall": 0.95, + "min_normal_samples": 300, + "min_phishing_samples": 60 + }, + "edges": { + "normal_max": 0.09204546158496832, + "phishing_min": 0.9185437473223178 + }, + "judging_set": { + "fingerprint": "55cd7d5b2687d233443079676109d177ce3e6f893ca5c32293e7c56922318c16", + "sample_count": 387 + }, + "judging_split": "real_holdout", + "latency_environment": { + "cpu_count": 10, + "omp_num_threads": null + }, + "measured": { + "alert_false_positive_rate": 0.003289473684210526, + "alert_recall": 0.8072289156626506, + "by_band": { + "certain_normal": { + "normal_count": 191, + "normal_share": 0.6282894736842105, + "phishing_count": 3, + "phishing_share": 0.03614457831325301, + "sample_count": 194 + }, + "certain_phishing": { + "normal_count": 1, + "normal_share": 0.003289473684210526, + "phishing_count": 67, + "phishing_share": 0.8072289156626506, + "sample_count": 68 + }, + "uncertain": { + "normal_count": 112, + "normal_share": 0.3684210526315789, + "phishing_count": 13, + "phishing_share": 0.1566265060240964, + "sample_count": 125 + } + }, + "coverage_recall": 0.963855421686747, + "edges": { + "normal_max": 0.09204546158496832, + "phishing_min": 0.9185437473223178 + }, + "missed_phishing_rate": 0.03614457831325301, + "normal_count": 304, + "phishing_count": 83, + "sample_count": 387, + "uncertain_share": 0.32299741602067183 + }, + "p95_latency_ms": 31.532012499999997, + "selection_split": "train_oof", + "synthetic_reference": { + "note": "합성 스트레스 셋은 참고용이며 판정에 사용하지 않는다", + "sample_count": 123 + } +} diff --git a/data_science/SMSModel/reports/dataset_split_summary.json b/data_science/SMSModel/reports/dataset_split_summary.json index aaa910d..a0f0386 100644 --- a/data_science/SMSModel/reports/dataset_split_summary.json +++ b/data_science/SMSModel/reports/dataset_split_summary.json @@ -18,458 +18,454 @@ } }, "dataset": { - "group_count": 798, + "group_count": 809, "labels": { "normal": { - "count": 623, - "ratio": 0.67206 + "count": 645, + "ratio": 0.679663 }, "phishing": { "count": 304, - "ratio": 0.32794 + "ratio": 0.320337 } }, - "row_count": 927, + "row_count": 949, "sources": { "original": { - "count": 505, - "ratio": 0.544768 + "count": 520, + "ratio": 0.547945 }, "real_collected_v4": { - "count": 163, - "ratio": 0.175836 + "count": 164, + "ratio": 0.172813 }, "real_phishing_v6": { "count": 17, - "ratio": 0.018339 + "ratio": 0.017914 }, "synthetic_diversity_v2": { "count": 14, - "ratio": 0.015102 + "ratio": 0.014752 }, "synthetic_fp_stress_train": { "count": 1, - "ratio": 0.001079 + "ratio": 0.001054 }, "synthetic_hard_negative_v2": { "count": 12, - "ratio": 0.012945 + "ratio": 0.012645 }, "synthetic_normal_v3": { - "count": 205, - "ratio": 0.221143 + "count": 211, + "ratio": 0.222339 }, "user_added": { "count": 10, - "ratio": 0.010787 + "ratio": 0.010537 } }, "types": { "결제환불사칭": { "count": 2, - "ratio": 0.002157 + "ratio": 0.002107 }, "경조사사칭": { "count": 64, - "ratio": 0.06904 + "ratio": 0.067439 }, "계정정지본인인증유도": { "count": 3, - "ratio": 0.003236 + "ratio": 0.003161 }, "금융기관사칭": { "count": 40, - "ratio": 0.04315 + "ratio": 0.04215 }, "기타정상": { "count": 42, - "ratio": 0.045307 + "ratio": 0.044257 }, "기타피싱": { "count": 2, - "ratio": 0.002157 + "ratio": 0.002107 }, "대출사기": { "count": 5, - "ratio": 0.005394 + "ratio": 0.005269 }, "복합피싱": { "count": 3, - "ratio": 0.003236 + "ratio": 0.003161 }, "수사기관사칭": { "count": 1, - "ratio": 0.001079 + "ratio": 0.001054 }, "악성링크앱설치유도": { "count": 4, - "ratio": 0.004315 + "ratio": 0.004215 }, "이벤트당첨사칭": { "count": 77, - "ratio": 0.083064 + "ratio": 0.081138 }, "일상대화": { "count": 139, - "ratio": 0.149946 + "ratio": 0.14647 }, "정부공공기관사칭": { "count": 29, - "ratio": 0.031284 + "ratio": 0.030558 }, "정상공공기관알림": { "count": 41, - "ratio": 0.044229 + "ratio": 0.043203 }, "정상광고프로모션": { - "count": 118, - "ratio": 0.127292 + "count": 126, + "ratio": 0.132771 }, "정상금융알림": { "count": 41, - "ratio": 0.044229 + "ratio": 0.043203 }, "정상인증알림": { - "count": 59, - "ratio": 0.063646 + "count": 60, + "ratio": 0.063224 }, "정상카드결제알림": { - "count": 49, - "ratio": 0.052859 + "count": 55, + "ratio": 0.057956 }, "정상택배배송안내": { - "count": 88, - "ratio": 0.09493 + "count": 95, + "ratio": 0.100105 }, "정상포인트소멸알림": { "count": 46, - "ratio": 0.049622 + "ratio": 0.048472 }, "중고거래사기": { "count": 6, - "ratio": 0.006472 + "ratio": 0.006322 }, "지인가족사칭": { "count": 18, - "ratio": 0.019417 + "ratio": 0.018967 }, "채용부업사기": { "count": 7, - "ratio": 0.007551 + "ratio": 0.007376 }, "택배배송사칭": { "count": 38, - "ratio": 0.040992 + "ratio": 0.040042 }, "투자리딩방사기": { "count": 5, - "ratio": 0.005394 + "ratio": 0.005269 } }, "unresolved_other_phishing_count": 2 }, - "dataset_fingerprint": "6fb005ba0fb276bbde36b745d4e863d7f579dc125cb30d31ad07b8c510317d36", + "dataset_fingerprint": "500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3", "schema_version": 1, "splits": { "test": { - "dataset_ratio": 0.147789, - "group_count": 122, + "dataset_ratio": 0.142255, + "group_count": 125, "labels": { "normal": { "count": 92, - "ratio": 0.671533 + "ratio": 0.681481 }, "phishing": { - "count": 45, - "ratio": 0.328467 + "count": 43, + "ratio": 0.318519 } }, "largest_group_size": 5, - "row_count": 137, + "row_count": 135, "sources": { "original": { - "count": 75, - "ratio": 0.547445 + "count": 69, + "ratio": 0.511111 }, "real_collected_v4": { - "count": 25, - "ratio": 0.182482 + "count": 27, + "ratio": 0.2 }, "real_phishing_v6": { - "count": 3, - "ratio": 0.021898 + "count": 2, + "ratio": 0.014815 }, "synthetic_diversity_v2": { "count": 2, - "ratio": 0.014599 - }, - "synthetic_fp_stress_train": { - "count": 1, - "ratio": 0.007299 + "ratio": 0.014815 }, "synthetic_hard_negative_v2": { "count": 3, - "ratio": 0.021898 + "ratio": 0.022222 }, "synthetic_normal_v3": { - "count": 27, - "ratio": 0.19708 + "count": 30, + "ratio": 0.222222 }, "user_added": { - "count": 1, - "ratio": 0.007299 + "count": 2, + "ratio": 0.014815 } }, "types": { "결제환불사칭": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 }, "경조사사칭": { - "count": 10, - "ratio": 0.072993 + "count": 9, + "ratio": 0.066667 }, "계정정지본인인증유도": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 }, "금융기관사칭": { - "count": 6, - "ratio": 0.043796 + "count": 5, + "ratio": 0.037037 }, "기타정상": { "count": 6, - "ratio": 0.043796 + "ratio": 0.044444 }, "기타피싱": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 }, "대출사기": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 }, "복합피싱": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 }, "악성링크앱설치유도": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 }, "이벤트당첨사칭": { - "count": 10, - "ratio": 0.072993 + "count": 9, + "ratio": 0.066667 }, "일상대화": { "count": 21, - "ratio": 0.153285 + "ratio": 0.155556 }, "정부공공기관사칭": { "count": 4, - "ratio": 0.029197 + "ratio": 0.02963 }, "정상공공기관알림": { - "count": 7, - "ratio": 0.051095 + "count": 6, + "ratio": 0.044444 }, "정상광고프로모션": { "count": 18, - "ratio": 0.131387 + "ratio": 0.133333 }, "정상금융알림": { "count": 4, - "ratio": 0.029197 + "ratio": 0.02963 }, "정상인증알림": { "count": 9, - "ratio": 0.065693 + "ratio": 0.066667 }, "정상카드결제알림": { - "count": 7, - "ratio": 0.051095 + "count": 8, + "ratio": 0.059259 }, "정상택배배송안내": { "count": 13, - "ratio": 0.094891 + "ratio": 0.096296 }, "정상포인트소멸알림": { "count": 7, - "ratio": 0.051095 + "ratio": 0.051852 }, "중고거래사기": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 }, "지인가족사칭": { "count": 2, - "ratio": 0.014599 + "ratio": 0.014815 }, "채용부업사기": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 }, "택배배송사칭": { - "count": 4, - "ratio": 0.029197 + "count": 5, + "ratio": 0.037037 }, "투자리딩방사기": { "count": 1, - "ratio": 0.007299 + "ratio": 0.007407 } } }, "train": { - "dataset_ratio": 0.705502, - "group_count": 555, + "dataset_ratio": 0.714436, + "group_count": 562, "labels": { "normal": { - "count": 439, - "ratio": 0.671254 + "count": 461, + "ratio": 0.679941 }, "phishing": { - "count": 215, - "ratio": 0.328746 + "count": 217, + "ratio": 0.320059 } }, "largest_group_size": 11, - "row_count": 654, + "row_count": 678, "sources": { "original": { - "count": 356, - "ratio": 0.544343 + "count": 386, + "ratio": 0.569322 }, "real_collected_v4": { - "count": 114, - "ratio": 0.174312 + "count": 112, + "ratio": 0.165192 }, "real_phishing_v6": { - "count": 12, - "ratio": 0.018349 + "count": 13, + "ratio": 0.019174 }, "synthetic_diversity_v2": { - "count": 9, - "ratio": 0.013761 + "count": 6, + "ratio": 0.00885 }, "synthetic_hard_negative_v2": { - "count": 8, - "ratio": 0.012232 + "count": 5, + "ratio": 0.007375 }, "synthetic_normal_v3": { - "count": 148, - "ratio": 0.2263 + "count": 150, + "ratio": 0.221239 }, "user_added": { - "count": 7, - "ratio": 0.010703 + "count": 6, + "ratio": 0.00885 } }, "types": { "결제환불사칭": { "count": 1, - "ratio": 0.001529 + "ratio": 0.001475 }, "경조사사칭": { - "count": 45, - "ratio": 0.068807 + "count": 46, + "ratio": 0.067847 }, "계정정지본인인증유도": { "count": 1, - "ratio": 0.001529 + "ratio": 0.001475 }, "금융기관사칭": { "count": 29, - "ratio": 0.044343 + "ratio": 0.042773 }, "기타정상": { "count": 30, - "ratio": 0.045872 + "ratio": 0.044248 }, "기타피싱": { "count": 1, - "ratio": 0.001529 + "ratio": 0.001475 }, "대출사기": { "count": 3, - "ratio": 0.004587 + "ratio": 0.004425 }, "복합피싱": { "count": 1, - "ratio": 0.001529 + "ratio": 0.001475 }, "수사기관사칭": { "count": 1, - "ratio": 0.001529 + "ratio": 0.001475 }, "악성링크앱설치유도": { "count": 2, - "ratio": 0.003058 + "ratio": 0.00295 }, "이벤트당첨사칭": { - "count": 56, - "ratio": 0.085627 + "count": 55, + "ratio": 0.081121 }, "일상대화": { "count": 97, - "ratio": 0.148318 + "ratio": 0.143068 }, "정부공공기관사칭": { "count": 21, - "ratio": 0.03211 + "ratio": 0.030973 }, "정상공공기관알림": { - "count": 28, - "ratio": 0.042813 + "count": 29, + "ratio": 0.042773 }, "정상광고프로모션": { - "count": 82, - "ratio": 0.125382 + "count": 90, + "ratio": 0.132743 }, "정상금융알림": { "count": 33, - "ratio": 0.050459 + "ratio": 0.048673 }, "정상인증알림": { - "count": 41, - "ratio": 0.062691 + "count": 42, + "ratio": 0.061947 }, "정상카드결제알림": { - "count": 34, - "ratio": 0.051988 + "count": 39, + "ratio": 0.057522 }, "정상택배배송안내": { - "count": 62, - "ratio": 0.094801 + "count": 69, + "ratio": 0.10177 }, "정상포인트소멸알림": { "count": 32, - "ratio": 0.04893 + "ratio": 0.047198 }, "중고거래사기": { "count": 4, - "ratio": 0.006116 + "ratio": 0.0059 }, "지인가족사칭": { "count": 14, - "ratio": 0.021407 + "ratio": 0.020649 }, "채용부업사기": { "count": 5, - "ratio": 0.007645 + "ratio": 0.007375 }, "택배배송사칭": { - "count": 28, - "ratio": 0.042813 + "count": 30, + "ratio": 0.044248 }, "투자리딩방사기": { "count": 3, - "ratio": 0.004587 + "ratio": 0.004425 } } }, "validation": { - "dataset_ratio": 0.14671, - "group_count": 121, + "dataset_ratio": 0.143309, + "group_count": 122, "labels": { "normal": { "count": 92, @@ -480,32 +476,36 @@ "ratio": 0.323529 } }, - "largest_group_size": 5, + "largest_group_size": 9, "row_count": 136, "sources": { "original": { - "count": 74, - "ratio": 0.544118 + "count": 65, + "ratio": 0.477941 }, "real_collected_v4": { - "count": 24, - "ratio": 0.176471 + "count": 25, + "ratio": 0.183824 }, "real_phishing_v6": { "count": 2, "ratio": 0.014706 }, "synthetic_diversity_v2": { - "count": 3, - "ratio": 0.022059 + "count": 6, + "ratio": 0.044118 }, - "synthetic_hard_negative_v2": { + "synthetic_fp_stress_train": { "count": 1, "ratio": 0.007353 }, + "synthetic_hard_negative_v2": { + "count": 4, + "ratio": 0.029412 + }, "synthetic_normal_v3": { - "count": 30, - "ratio": 0.220588 + "count": 31, + "ratio": 0.227941 }, "user_added": { "count": 2, @@ -522,8 +522,8 @@ "ratio": 0.007353 }, "금융기관사칭": { - "count": 5, - "ratio": 0.036765 + "count": 6, + "ratio": 0.044118 }, "기타정상": { "count": 6, @@ -542,8 +542,8 @@ "ratio": 0.007353 }, "이벤트당첨사칭": { - "count": 11, - "ratio": 0.080882 + "count": 13, + "ratio": 0.095588 }, "일상대화": { "count": 21, @@ -594,8 +594,8 @@ "ratio": 0.007353 }, "택배배송사칭": { - "count": 6, - "ratio": 0.044118 + "count": 3, + "ratio": 0.022059 }, "투자리딩방사기": { "count": 1, diff --git a/data_science/SMSModel/reports/dataset_split_summary.md b/data_science/SMSModel/reports/dataset_split_summary.md index 2aac0ad..c365a57 100644 --- a/data_science/SMSModel/reports/dataset_split_summary.md +++ b/data_science/SMSModel/reports/dataset_split_summary.md @@ -3,9 +3,9 @@ ## Dataset - Schema version: `1` -- Dataset fingerprint: `6fb005ba0fb276bbde36b745d4e863d7f579dc125cb30d31ad07b8c510317d36` -- Total rows: 927 -- Template groups: 798 +- Dataset fingerprint: `500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3` +- Total rows: 949 +- Template groups: 809 - Unresolved other phishing rows: 2 ## Configuration @@ -18,9 +18,9 @@ | Split | Rows | Ratio | Groups | Normal | Phishing | |---|---:|---:|---:|---:|---:| -| train | 654 | 70.55% | 555 | 439 (67.13%) | 215 (32.87%) | -| validation | 136 | 14.67% | 121 | 92 (67.65%) | 44 (32.35%) | -| test | 137 | 14.78% | 122 | 92 (67.15%) | 45 (32.85%) | +| train | 678 | 71.44% | 562 | 461 (67.99%) | 217 (32.01%) | +| validation | 136 | 14.33% | 122 | 92 (67.65%) | 44 (32.35%) | +| test | 135 | 14.23% | 125 | 92 (68.15%) | 43 (31.85%) | ## Leakage Validation @@ -35,30 +35,30 @@ | Type | Count | Ratio | |---|---:|---:| | 결제환불사칭 | 1 | 0.15% | -| 경조사사칭 | 45 | 6.88% | +| 경조사사칭 | 46 | 6.78% | | 계정정지본인인증유도 | 1 | 0.15% | -| 금융기관사칭 | 29 | 4.43% | -| 기타정상 | 30 | 4.59% | +| 금융기관사칭 | 29 | 4.28% | +| 기타정상 | 30 | 4.42% | | 기타피싱 | 1 | 0.15% | -| 대출사기 | 3 | 0.46% | +| 대출사기 | 3 | 0.44% | | 복합피싱 | 1 | 0.15% | | 수사기관사칭 | 1 | 0.15% | -| 악성링크앱설치유도 | 2 | 0.31% | -| 이벤트당첨사칭 | 56 | 8.56% | -| 일상대화 | 97 | 14.83% | -| 정부공공기관사칭 | 21 | 3.21% | -| 정상공공기관알림 | 28 | 4.28% | -| 정상광고프로모션 | 82 | 12.54% | -| 정상금융알림 | 33 | 5.05% | -| 정상인증알림 | 41 | 6.27% | -| 정상카드결제알림 | 34 | 5.20% | -| 정상택배배송안내 | 62 | 9.48% | -| 정상포인트소멸알림 | 32 | 4.89% | -| 중고거래사기 | 4 | 0.61% | -| 지인가족사칭 | 14 | 2.14% | -| 채용부업사기 | 5 | 0.76% | -| 택배배송사칭 | 28 | 4.28% | -| 투자리딩방사기 | 3 | 0.46% | +| 악성링크앱설치유도 | 2 | 0.29% | +| 이벤트당첨사칭 | 55 | 8.11% | +| 일상대화 | 97 | 14.31% | +| 정부공공기관사칭 | 21 | 3.10% | +| 정상공공기관알림 | 29 | 4.28% | +| 정상광고프로모션 | 90 | 13.27% | +| 정상금융알림 | 33 | 4.87% | +| 정상인증알림 | 42 | 6.19% | +| 정상카드결제알림 | 39 | 5.75% | +| 정상택배배송안내 | 69 | 10.18% | +| 정상포인트소멸알림 | 32 | 4.72% | +| 중고거래사기 | 4 | 0.59% | +| 지인가족사칭 | 14 | 2.06% | +| 채용부업사기 | 5 | 0.74% | +| 택배배송사칭 | 30 | 4.42% | +| 투자리딩방사기 | 3 | 0.44% | ### Validation @@ -66,12 +66,12 @@ |---|---:|---:| | 경조사사칭 | 9 | 6.62% | | 계정정지본인인증유도 | 1 | 0.74% | -| 금융기관사칭 | 5 | 3.68% | +| 금융기관사칭 | 6 | 4.41% | | 기타정상 | 6 | 4.41% | | 대출사기 | 1 | 0.74% | | 복합피싱 | 1 | 0.74% | | 악성링크앱설치유도 | 1 | 0.74% | -| 이벤트당첨사칭 | 11 | 8.09% | +| 이벤트당첨사칭 | 13 | 9.56% | | 일상대화 | 21 | 15.44% | | 정부공공기관사칭 | 4 | 2.94% | | 정상공공기관알림 | 6 | 4.41% | @@ -84,47 +84,47 @@ | 중고거래사기 | 1 | 0.74% | | 지인가족사칭 | 2 | 1.47% | | 채용부업사기 | 1 | 0.74% | -| 택배배송사칭 | 6 | 4.41% | +| 택배배송사칭 | 3 | 2.21% | | 투자리딩방사기 | 1 | 0.74% | ### Test | Type | Count | Ratio | |---|---:|---:| -| 결제환불사칭 | 1 | 0.73% | -| 경조사사칭 | 10 | 7.30% | -| 계정정지본인인증유도 | 1 | 0.73% | -| 금융기관사칭 | 6 | 4.38% | -| 기타정상 | 6 | 4.38% | -| 기타피싱 | 1 | 0.73% | -| 대출사기 | 1 | 0.73% | -| 복합피싱 | 1 | 0.73% | -| 악성링크앱설치유도 | 1 | 0.73% | -| 이벤트당첨사칭 | 10 | 7.30% | -| 일상대화 | 21 | 15.33% | -| 정부공공기관사칭 | 4 | 2.92% | -| 정상공공기관알림 | 7 | 5.11% | -| 정상광고프로모션 | 18 | 13.14% | -| 정상금융알림 | 4 | 2.92% | -| 정상인증알림 | 9 | 6.57% | -| 정상카드결제알림 | 7 | 5.11% | -| 정상택배배송안내 | 13 | 9.49% | -| 정상포인트소멸알림 | 7 | 5.11% | -| 중고거래사기 | 1 | 0.73% | -| 지인가족사칭 | 2 | 1.46% | -| 채용부업사기 | 1 | 0.73% | -| 택배배송사칭 | 4 | 2.92% | -| 투자리딩방사기 | 1 | 0.73% | +| 결제환불사칭 | 1 | 0.74% | +| 경조사사칭 | 9 | 6.67% | +| 계정정지본인인증유도 | 1 | 0.74% | +| 금융기관사칭 | 5 | 3.70% | +| 기타정상 | 6 | 4.44% | +| 기타피싱 | 1 | 0.74% | +| 대출사기 | 1 | 0.74% | +| 복합피싱 | 1 | 0.74% | +| 악성링크앱설치유도 | 1 | 0.74% | +| 이벤트당첨사칭 | 9 | 6.67% | +| 일상대화 | 21 | 15.56% | +| 정부공공기관사칭 | 4 | 2.96% | +| 정상공공기관알림 | 6 | 4.44% | +| 정상광고프로모션 | 18 | 13.33% | +| 정상금융알림 | 4 | 2.96% | +| 정상인증알림 | 9 | 6.67% | +| 정상카드결제알림 | 8 | 5.93% | +| 정상택배배송안내 | 13 | 9.63% | +| 정상포인트소멸알림 | 7 | 5.19% | +| 중고거래사기 | 1 | 0.74% | +| 지인가족사칭 | 2 | 1.48% | +| 채용부업사기 | 1 | 0.74% | +| 택배배송사칭 | 5 | 3.70% | +| 투자리딩방사기 | 1 | 0.74% | ## Source Distribution | Source | Count | Ratio | |---|---:|---:| -| original | 505 | 54.48% | -| real_collected_v4 | 163 | 17.58% | -| real_phishing_v6 | 17 | 1.83% | -| synthetic_diversity_v2 | 14 | 1.51% | +| original | 520 | 54.79% | +| real_collected_v4 | 164 | 17.28% | +| real_phishing_v6 | 17 | 1.79% | +| synthetic_diversity_v2 | 14 | 1.48% | | synthetic_fp_stress_train | 1 | 0.11% | -| synthetic_hard_negative_v2 | 12 | 1.29% | -| synthetic_normal_v3 | 205 | 22.11% | -| user_added | 10 | 1.08% | +| synthetic_hard_negative_v2 | 12 | 1.26% | +| synthetic_normal_v3 | 211 | 22.23% | +| user_added | 10 | 1.05% | diff --git a/data_science/SMSModel/reports/error_analysis_v9_finetuned_dedup_both.json b/data_science/SMSModel/reports/error_analysis_v9_finetuned_dedup_both.json new file mode 100644 index 0000000..53c3f2f --- /dev/null +++ b/data_science/SMSModel/reports/error_analysis_v9_finetuned_dedup_both.json @@ -0,0 +1,775 @@ +{ + "artifact_threshold": 0.09204546158496833, + "splits": [ + { + "extreme_samples": { + "highest_scoring_normals": [ + { + "probability": 0.9831475324770014, + "text_fingerprint": "af129827c3283f2218baef8752bf17dc48159f820b6793985bfcd7c83213f83b", + "type": "기타정상" + }, + { + "probability": 0.8428115102943464, + "text_fingerprint": "4179f54dfe26365994664664a8621684d1b47818b3588d81bd890449acf218ee", + "type": "기타정상" + }, + { + "probability": 0.6869388465465591, + "text_fingerprint": 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"phishing_count": 83, + "pr_auc": 0.9480830447750438, + "roc_auc": 0.9658766645529486, + "sample_count": 387, + "split": "real_holdout" + } + ], + "structural_feature_names": [ + "naive_bayes_score", + "logistic_regression_score", + "linear_svm_score", + "korean_encoder_score", + "has_url", + "has_short_url", + "has_phone", + "has_account", + "has_card", + "has_amount", + "has_web_tag", + "has_urgency", + "has_transfer_request", + "has_personal_info_request", + "has_link_action", + "is_long_text", + "has_opt_out", + "has_family_impersonation", + "has_chatroom_invite", + "has_job_offer_lure", + "has_investment_lure" + ] +} diff --git a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.json b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.json index f09756c..d933ab5 100644 --- a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.json +++ b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.json @@ -1,9 +1,6 @@ { "error_samples": { "false_negatives": [ - { - "text_fingerprint": "057af781b64233fc26bb19129616eaefc14d5d78ea7b771e85c2daaf33cc0d11" - }, { "text_fingerprint": "102ecec1e3c66b3d3b2fee43a9b766dfbeea6807a2862be01d6e115b97ab909e" } @@ -15,6 +12,12 @@ { "text_fingerprint": "63fac6a7e948aa2daef3f106f0315a48e2361f6cd07047ce725966ccd42075cc" }, + { + "text_fingerprint": "52b243ae5b2c5906b0f4edf3661eb1c72f176d74dfc9b8c8858fdd93929dae64" + }, + { + "text_fingerprint": "3352ae1589dfba566e596eff5e5620e618998b4095267437e6b5e8b0bbc6b541" + }, { "text_fingerprint": "82bf23dc4e2ea1b0f2e88068e1bf3e139595263f342cc419c3cd0d006a357e22" }, @@ -33,18 +36,15 @@ { "text_fingerprint": "8853af0f6c41fd563514f394e1ec8009fca3837827c0b68f1e7b28ed940d51f0" }, - { - "text_fingerprint": "13ba85cb5b06529e2a3dae04f316a6ae2129c0dfec1f2092016c24ee94bbf180" - }, { "text_fingerprint": "10adc14bcef5e4426f96c073a9574376d71bf0594276cdd5d47422cd875f88cc" } ] }, "latency_stats": { - "mean": 10.656899007407405, - "p50": 9.48575, - "p95": 15.940691799999996, + "mean": 10.564942925925926, + "p50": 9.387291, + "p95": 15.340725099999998, "sample_count": 135 }, "normal_by_type": { @@ -69,14 +69,14 @@ "true_negative": 20 }, "정상공공기관알림": { - "false_positive": 1, - "false_positive_rate": 0.16666666666666666, + "false_positive": 2, + "false_positive_rate": 0.3333333333333333, "false_positive_rate_95_ci": { - "lower": 0.03005258587173032, - "upper": 0.563509436563646 + "lower": 0.09676933255921683, + "upper": 0.7000116786584712 }, "sample_count": 6, - "true_negative": 5 + "true_negative": 4 }, "정상광고프로모션": { "false_positive": 1, @@ -109,14 +109,14 @@ "true_negative": 9 }, "정상카드결제알림": { - "false_positive": 1, - "false_positive_rate": 0.125, + "false_positive": 2, + "false_positive_rate": 0.25, "false_positive_rate_95_ci": { - "lower": 0.02241690886329617, - "upper": 0.4708948057698615 + "lower": 0.07147768885802763, + "upper": 0.5907301208974108 }, "sample_count": 8, - "true_negative": 7 + "true_negative": 6 }, "정상택배배송안내": { "false_positive": 1, @@ -129,28 +129,28 @@ "true_negative": 12 }, "정상포인트소멸알림": { - "false_positive": 2, - "false_positive_rate": 0.2857142857142857, + "false_positive": 1, + "false_positive_rate": 0.14285714285714285, "false_positive_rate_95_ci": { - "lower": 0.0822171657090155, - "upper": 0.6410709098517873 + "lower": 0.02567895594897479, + "upper": 0.51313450331903 }, "sample_count": 7, - "true_negative": 5 + "true_negative": 6 } }, "overall_metrics": { "accuracy": 0.9111111111111111, "confusion_matrix": { - "false_negative": 2, - "false_positive": 10, - "true_negative": 82, - "true_positive": 41 + "false_negative": 1, + "false_positive": 11, + "true_negative": 81, + "true_positive": 42 }, - "f1_score": 0.8723404255319148, - "f2_score": 0.9192825112107623, - "precision": 0.803921568627451, - "recall": 0.9534883720930233 + "f1_score": 0.875, + "f2_score": 0.9333333333333332, + "precision": 0.7924528301886793, + "recall": 0.9767441860465116 }, "phishing_by_type": { "결제환불사칭": { @@ -274,14 +274,14 @@ "true_positive": 2 }, "채용부업사기": { - "false_negative": 1, - "recall": 0.0, + "false_negative": 0, + "recall": 1.0, "recall_95_ci": { - "lower": 0.0, - "upper": 0.7934567085261071 + "lower": 0.20654329147389294, + "upper": 1.0 }, "sample_count": 1, - "true_positive": 0 + "true_positive": 1 }, "택배배송사칭": { "false_negative": 0, @@ -316,29 +316,29 @@ "schema_version": 1, "shared_metrics": { "accuracy": 0.9111111111111111, - "f1": 0.8723404255319149, - "f2": 0.9192825112107623, - "false_negative": 2, - "false_positive": 10, - "precision": 0.803921568627451, - "recall": 0.9534883720930233, + "f1": 0.875, + "f2": 0.9333333333333333, + "false_negative": 1, + "false_positive": 11, + "precision": 0.7924528301886793, + "recall": 0.9767441860465116, "sample_count": 135, - "true_negative": 82, - "true_positive": 41 + "true_negative": 81, + "true_positive": 42 }, "split": "test", - "threshold": 0.1638286294849425, + "threshold": 0.12388533458915509, "unavailable_models": [], "unique_template_metrics": { "accuracy": 0.904, - "f1_score": 0.8421052631578947, - "f2_score": 0.8988764044943819, - "false_negative": 2, - "false_positive": 10, - "precision": 0.7619047619047619, - "recall": 0.9411764705882353, + "f1_score": 0.846153846153846, + "f2_score": 0.9166666666666666, + "false_negative": 1, + "false_positive": 11, + "precision": 0.75, + "recall": 0.9705882352941176, "sample_count": 125, - "true_negative": 81, - "true_positive": 32 + "true_negative": 80, + "true_positive": 33 } } diff --git a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md index d5f1da1..f57d01e 100644 --- a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md +++ b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md @@ -6,14 +6,14 @@ Threshold selection: validation only; final metrics: test only. |---|---:| | Samples | 135 | | Accuracy | 0.911111 | -| Precision | 0.803922 | -| Recall | 0.953488 | -| F1 | 0.872340 | -| F2 | 0.919283 | -| TN | 82 | -| FP | 10 | -| FN | 2 | -| TP | 41 | -| Mean latency (ms) | 10.657 | -| P50 latency (ms) | 9.486 | -| P95 latency (ms) | 15.941 | +| Precision | 0.792453 | +| Recall | 0.976744 | +| F1 | 0.875000 | +| F2 | 0.933333 | +| TN | 81 | +| FP | 11 | +| FN | 1 | +| TP | 42 | +| Mean latency (ms) | 10.565 | +| P50 latency (ms) | 9.387 | +| P95 latency (ms) | 15.341 | diff --git a/data_science/SMSModel/run_stacking_training.py b/data_science/SMSModel/run_stacking_training.py index 6ee3e65..402ffcb 100644 --- a/data_science/SMSModel/run_stacking_training.py +++ b/data_science/SMSModel/run_stacking_training.py @@ -70,17 +70,17 @@ # 사용자 대면 오탐 보장은 판정셋에서 alert_false_positive_rate가 담당한다. MAX_NORMAL_FALSE_POSITIVE_RATE = 0.10 EXPECTED_DATASET_FINGERPRINT = ( - "6fb005ba0fb276bbde36b745d4e86" - "3d7f579dc125cb30d31ad07b8c510317d36" + "500884cc4bd2ed9a45ce8b7dfbd16a" + "ff6c7672422bbe7959f5b190fcdc98b1b3" ) -EXPECTED_TOTAL_CSV_ROWS = 3540 -EXPECTED_TRAINING_POOL_ROWS = 927 +EXPECTED_TOTAL_CSV_ROWS = 3562 +EXPECTED_TRAINING_POOL_ROWS = 949 EXPECTED_HOLDOUT_ROWS = 510 EXPECTED_SPLIT_COUNTS = { - "train": 654, + "train": 678, "validation": 136, - "test": 137, + "test": 135, } diff --git a/data_science/SMSModel/splits/sms_split_v6.csv b/data_science/SMSModel/splits/sms_split_v6.csv index 9b24e25..757fccf 100644 --- a/data_science/SMSModel/splits/sms_split_v6.csv +++ b/data_science/SMSModel/splits/sms_split_v6.csv @@ -1,163 +1,151 @@ text_fingerprint,template_group_id,split,label,type -089f2ecf7a0060ec4c6a76b634d28cf6528a77174e505f05eee612db99e58440,tpl_089f2ecf7a00,test,normal,정상공공기관알림 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+f9ad1d14420a31d50d6b8389aa19addc3ae85cc93e4315b97756a883c1ef8ec2,tpl_f9ad1d14420a,validation,normal,일상대화 +fb62fd639af13c2aa2e7cfc6de16ba6759cca9b6bd1a9ca114107643ad2dcdf9,tpl_fb62fd639af1,validation,phishing,정부공공기관사칭 +fb6bf8fe74014472ced1fb53fac643a390dcb3553e04f7605398bcb7ea0b6b48,tpl_fb6bf8fe7401,validation,normal,정상광고프로모션 +fccbe373b0203ed39c7941cc7107a5e7e88b4cc2458045d781c39d296268ce62,tpl_fccbe373b020,validation,normal,정상금융알림 diff --git a/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py b/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py index 4c5f27f..1b21217 100644 --- a/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py +++ b/tests/data_science/SMSModel/modeling/test_korean_encoder_finetuned.py @@ -263,15 +263,19 @@ def test_metadata_records_the_fine_tuning_settings( assert metadata["fine_tuning"]["class_weight"] == "balanced" -def test_constant_learning_rate_is_the_default( - classifier: FineTunedKoreanEncoderClassifier, -) -> None: - """스케줄러 도입이 기존 artifact의 재현성을 깨면 안 된다. - - warmup_ratio 기본값 0은 스케줄러를 아예 만들지 않아 고정 학습률로 - 학습하던 이전 동작을 그대로 유지한다. +def test_constant_learning_rate_is_the_default() -> None: + """warmup은 판정셋에서 오히려 나빠져 기본값에서 뺐다(#102). + + validation에서는 warmup 0.1 + lr 5e-5가 가장 좋아 보였으나 + (AUC 0.9921 -> 0.9951) 판정셋에서는 uncertain_normal_share가 + 0.1908 -> 0.2928로, alert_false_positive_rate가 0.0033 -> 0.0230으로 + 나빠졌다. validation이 판정셋의 대리치가 못 된다는 뜻이라, 측정되지 + 않은 개선을 기본값으로 굳히지 않는다. """ + classifier = FineTunedKoreanEncoderClassifier() + assert classifier.warmup_ratio == 0.0 + assert classifier.learning_rate == 2e-5 assert classifier._build_scheduler(object(), row_count=100) is None assert classifier.get_metadata()["fine_tuning"]["lr_schedule"] == "constant" From 4966cc66e877a9dfa46f9abd6e91418add98fa68 Mon Sep 17 00:00:00 2001 From: Gibeom Date: Sun, 23 Aug 2026 12:24:33 +0900 Subject: [PATCH 6/7] =?UTF-8?q?Docs(#102):=20=EC=B1=84=ED=83=9D=20?= =?UTF-8?q?=EA=B8=B0=EC=A4=80=20v9=20=ED=8C=90=EC=A0=95=20=EA=B0=B1?= =?UTF-8?q?=EC=8B=A0=20=EB=B0=8F=20=EB=AF=B8=EB=8B=AC=20=EC=9B=90=EC=9D=B8?= =?UTF-8?q?=20=ED=99=95=EC=A0=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 문서 6장 절차대로 v9 판정을 5장에 기록하고, v6·v7 판정은 이력으로 남긴다. 기준값은 건드리지 않았다 - 6장이 미달 확인 후 완화를 금지한다. ## 판정: FAIL (미달 1건, 판정 보류 1건) | 항목 | v7 | v9 | 기준 | v9 | |---|---:|---:|---:|---| | 피싱 포착률 | 0.8795 | 0.9518 | ≥0.95 | 통과(최초) | | 자동 경고 오탐률 | 0.0000 | 0.0066 | ≤0.01 | 통과 | | '의심' 정상 비율 | 0.1201 | 0.1908 | ≤0.133 | 미달 | | P95 지연시간 | 54.89ms | 측정 불가 | ≤50ms | 판정 보류 | '의심' 정상 비율이 v7보다 나빠 보이지만 두 지표는 경계를 사이에 둔 트레이드오프다. v7은 경계를 낮춰 의심 구간을 좁힌 대신 피싱을 놓쳤고, v9는 포착률을 확보한 대가로 의심 구간이 넓어졌다. 임계값과 무관한 지표로 보면 PR-AUC가 0.9250 -> 0.9448로 올랐고, 유형별 오탐률은 전 구간에서 개선됐다(전체 43.8% -> 19.7%, 정상택배 78.6% -> 32.1% 등). ## 미달 원인은 데이터로 확정 6장 2항이 요구하는 원인 분리를 실측으로 수행했다. - 임계값 정책 아님 - 경계를 조합해 훑어도 두 기준을 동시에 만족하는 지점이 하나도 없다. - 모델 아님 - fold 5->10, 기관 대조 신호 메타 피처, 정상 데이터 중복 제거, warmup 스케줄러 네 가지를 모두 실측으로 기각했다. 뒤의 둘은 오히려 악화시켰다. - 데이터임 - 오탐이 남은 유형은 실수집 데이터가 7~18건뿐이고, 실데이터 비율과 오탐률이 그대로 역상관이다(일상대화 실데이터 98%/오탐 6.1% vs 정상카드결제 13%/29.6%). 같은 이유로 선정용 split도 만들 수 없다. validation이 학습 pool에서 뽑히는데 그 60%가 쉬운 증강분이라, 하이퍼파라미터 7개 구성 전부에서 정상의 96.7~98.9%가 경계 아래로 내려가 변별이 안 됐다. 실제로 validation에서 가장 좋아 보이던 설정이 판정셋에서는 더 나빴다. ## 지연시간은 통과로 기록하지 않는다 배포 환경(t3.small)에 접근할 수 없어 2.6절 조건으로 측정하지 못했다. 개발 장비에서 OMP_NUM_THREADS=2로 재면 21.56ms지만 이는 애플 실리콘에서 스레드만 제한한 값이다. v7도 같은 방식으로는 38.22ms였으나 실제 배포 환경에서는 54.89ms였다. 파인튜닝은 forward 연산량을 바꾸지 않으므로 v9도 같은 제약을 받는다. ## 그 외 - 5.4절: 인코더를 뺀 구성(NB+LR+SVM)을 측정해 남겼다. 개선의 28%만 데이터 정리 몫이고 72%가 인코더 몫이며, 인코더를 빼면 자동 경고 오탐률까지 미달해 항목이 둘로 는다. - 5.7절: 6장 3항대로 수집 이슈를 분리했다. 유형별 필요 수량과, 합성으로 대체할 수 없는 이유를 실측 근거와 함께 적었다. - dataset_splitting에 selection_source 옵션을 추가했으나 켜지 않았다. original 우선으로 채워도 48% -> 58%에서 천장을 치고, 지금 켜면 split이 바뀌어 5장 수치만 무효가 된다. --- .../stacking/v9-experiment/metadata.json | 16 +- .../SMSModel/dataset_splitting/config.py | 20 + .../SMSModel/dataset_splitting/splitter.py | 33 +- .../SMSModel/reports/adoption_gate_v9.json | 113 +++ .../SMSModel/reports/error_analysis_v9.json | 775 ++++++++++++++++++ .../test_evaluation.json | 108 +-- .../stacking_v9-experiment/test_evaluation.md | 22 +- data_science/SMSModel/train_sms.py | 7 + docs/STANDALONE_ADOPTION_CRITERIA.md | 147 +++- 9 files changed, 1138 insertions(+), 103 deletions(-) create mode 100644 data_science/SMSModel/reports/adoption_gate_v9.json create mode 100644 data_science/SMSModel/reports/error_analysis_v9.json diff --git a/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json b/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json index 4dfaa64..82e5c4a 100644 --- a/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json +++ b/data_science/SMSModel/artifacts/stacking/v9-experiment/metadata.json @@ -1,6 +1,6 @@ { "artifact_version": "v9", - "created_at": "2026-08-23T01:53:44.810570+00:00", + "created_at": "2026-08-23T03:19:14.349356+00:00", "dataset": { "dataset_fingerprint": "500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3", "holdout_rows": 510, @@ -50,10 +50,10 @@ "model_name": "stacking_phishing_classifier", "oof_splits": 10, "random_state": 42, - "threshold": 0.12388533458915509 + "threshold": 0.1638286294849425 }, - "model_configuration_sha256": "026703bcc1f752854d5ffc2672e8fba5520d4d4f6a3037102cbcc2096f06992a", - "model_sha256": "47c0d6c104d1d6e9ac95812814970cac9a092f8124ac20918e65fd1f1514b06e", + "model_configuration_sha256": "f664233b082d1ddae8ae1c58954a4acf858eff6c8e016c89142852b5eba3d17a", + "model_sha256": "a2c9d5a4340f61662db2fa2256632ba5298c96f43ca98c7dc6f6ab22d900e8a3", "schema_version": 2, "split_manifest": "sms_split_v6.csv", "split_manifest_sha256": "214dd3385689f18907720b8f29c8710390672c7f3410fe3acd1af1ee74af17b2", @@ -71,9 +71,9 @@ "training_split": "train" }, "validation": { - "f2": 0.8930112165660051, - "false_positive_rate": 0.1822125813449024, - "max_false_positive_rate": 0.1822125813449024, + "f2": 0.9094903339191565, + "false_positive_rate": 0.13665943600867678, + "max_false_positive_rate": 0.13665943600867678, "measurable_false_positive_rate": 0.0021691973969631237, "recall": 0.9539170506912442, "target_recall": 0.95, @@ -82,7 +82,7 @@ "validation_reference": { "false_positive_rate": 0.09782608695652174, "normal_count": 92, - "recall": 1.0, + "recall": 0.9318181818181818, "sample_count": 136 } } diff --git a/data_science/SMSModel/dataset_splitting/config.py b/data_science/SMSModel/dataset_splitting/config.py index 46d8cb2..89054ad 100644 --- a/data_science/SMSModel/dataset_splitting/config.py +++ b/data_science/SMSModel/dataset_splitting/config.py @@ -26,6 +26,22 @@ class DatasetSplitConfig: # 적은 유형이 한쪽 split에서 통째로 빠지기 쉬워 함께 평가한다. stratified_candidate_count: int = 200 + # 임계값·경계 선정에 쓰는 split을 특정 출처 위주로 채우기 위한 설정. + # + # validation은 학습 pool에서 무작위로 뽑히는데, pool 정상의 60%가 증강 + # 데이터(synthetic_normal_v3, real_collected_v4)라 실제 수집 문자보다 + # 훨씬 쉬웠다. 그 결과 validation이 포화돼(정상 96.7~98.9%가 경계 아래) + # 하이퍼파라미터 구성 간 차이가 표본 1건 수준으로 뭉개졌고, 실제로 + # validation에서 가장 좋아 보이던 설정이 판정셋에서는 더 나빴다(#102). + # + # 출처별 난이도(OOF 실측, 정상 평균 확률): + # synthetic_normal_v3 0.099 < real_collected_v4 0.073 < original 0.142 + # 판정셋(real_holdout) 0.179 + # original이 판정셋에 가장 가까워 선정용 split을 이쪽으로 몰아준다. + selection_source_column: str | None = "source" + selection_source: str | None = None + selection_source_weight: float = 0.0 + def __post_init__(self) -> None: total_size = self.train_size + self.val_size + self.test_size if abs(total_size - 1.0) > 1e-9: @@ -44,4 +60,8 @@ def __post_init__(self) -> None: if self.stratified_candidate_count < 0: raise ValueError( "stratified_candidate_count must not be negative" + ) + if self.selection_source_weight < 0.0: + raise ValueError( + "selection_source_weight must not be negative" ) \ No newline at end of file diff --git a/data_science/SMSModel/dataset_splitting/splitter.py b/data_science/SMSModel/dataset_splitting/splitter.py index c56a0c8..06610db 100644 --- a/data_science/SMSModel/dataset_splitting/splitter.py +++ b/data_science/SMSModel/dataset_splitting/splitter.py @@ -1,7 +1,7 @@ """그룹 보존과 클래스·유형 비율 최적화를 적용한 데이터 분할""" from __future__ import annotations -from dataclasses import dataclass +from dataclasses import dataclass, replace import numpy as np import pandas as pd @@ -91,6 +91,9 @@ def _candidate_score( type_column: str | None, type_values: list[str], type_weight: float, + selection_source_column: str | None = None, + selection_source: str | None = None, + selection_source_weight: float = 0.0, ) -> tuple[float, int, float, float]: """목표 행 비율과 클래스·유형 분포에 가까울수록 낮은 점수를 반환""" size_error = abs((len(selected) / len(full_data)) - target_size) @@ -120,6 +123,23 @@ def _candidate_score( values=type_values, ) + # 선정용 split은 지정 출처(실수집 데이터)로 채울수록 좋다. 유형 커버리지와 + # 크기를 먼저 맞춘 뒤, 같은 조건이면 지정 출처 비율이 높은 후보를 고른다. + if ( + selection_source is not None + and selection_source_column is not None + and selection_source_column in selected.columns + and selection_source_weight > 0.0 + and len(selected) > 0 + ): + off_source_share = float( + ( + selected[selection_source_column].astype(str) + != selection_source + ).mean() + ) + distribution_error += selection_source_weight * off_source_share + # 유형이 통째로 빠지는 것을 가장 먼저 막고, 그다음 크기와 분포를 맞춘다. # 크기는 1%p 단위 등급으로 비교해 미세한 차이로 후보가 뒤집히지 않게 한다. size_error_bucket = int(size_error / 0.01) @@ -270,6 +290,9 @@ def _select_best_group_split( type_column=type_column, type_values=type_values, type_weight=config.type_weight, + selection_source_column=config.selection_source_column, + selection_source=config.selection_source, + selection_source_weight=config.selection_source_weight, ) if score < best_score: best_score = score @@ -308,13 +331,19 @@ def split_grouped_dataset( if df[config.group_column].nunique() < 3: raise ValueError("at least three template groups are required") + # test는 최종 평가용이라 전체 분포를 그대로 닮아야 한다 - 출처 선호를 걸지 않는다. train_validation, test = _select_best_group_split( df, selected_size=config.test_size, - config=config, + config=replace( + config, + selection_source=None, + selection_source_weight=0.0, + ), random_state_offset=0, ) relative_validation_size = config.val_size / (config.train_size + config.val_size) + # validation은 임계값·경계 선정에 쓰이므로 판정셋과 난이도가 비슷해야 한다. train, validation = _select_best_group_split( train_validation, selected_size=relative_validation_size, diff --git a/data_science/SMSModel/reports/adoption_gate_v9.json b/data_science/SMSModel/reports/adoption_gate_v9.json new file mode 100644 index 0000000..130d632 --- /dev/null +++ b/data_science/SMSModel/reports/adoption_gate_v9.json @@ -0,0 +1,113 @@ +{ + "assessment": { + "criteria": [ + { + "detail": "오탐 0건이 상한 준수의 증거가 되려면 정상 표본이 필요하다", + "measured": 304.0, + "name": "normal_sample_size", + "outcome": "PASS", + "required": 300.0 + }, + { + "detail": "놓침 상한을 확인하는 데 필요한 피싱 표본", + "measured": 83.0, + "name": "phishing_sample_size", + "outcome": "PASS", + "required": 60.0 + }, + { + "detail": "최소 '의심' 이상으로 걸러낸 피싱 비율", + "measured": 0.9518072289156626, + "name": "coverage_recall", + "outcome": "PASS", + "required": 0.95 + }, + { + "detail": "자동 경고가 잘못 울린 정상 비율", + "measured": 0.006578947368421052, + "name": "alert_false_positive_rate", + "outcome": "PASS", + "required": 0.01 + }, + { + "detail": "'의심' 카드가 뜬 정상 비율", + "measured": 0.19078947368421054, + "name": "uncertain_normal_share", + "outcome": "FAIL", + "required": 0.133 + }, + { + "detail": "단건 추론 P95 지연시간", + "measured": 21.557700099999998, + "name": "p95_latency_ms", + "outcome": "PASS", + "required": 50.0 + } + ], + "verdict": "FAIL" + }, + "criteria": { + "max_alert_false_positive_rate": 0.01, + "max_p95_latency_ms": 50.0, + "max_uncertain_normal_share": 0.133, + "min_coverage_recall": 0.95, + "min_normal_samples": 300, + "min_phishing_samples": 60 + }, + "edges": { + "normal_max": 0.16382862948494248, + "phishing_min": 0.8733913630982381 + }, + "judging_set": { + "fingerprint": "55cd7d5b2687d233443079676109d177ce3e6f893ca5c32293e7c56922318c16", + "sample_count": 387 + }, + "judging_split": "real_holdout", + "latency_environment": { + "cpu_count": 10, + "omp_num_threads": "2" + }, + "measured": { + "alert_false_positive_rate": 0.006578947368421052, + "alert_recall": 0.8072289156626506, + "by_band": { + "certain_normal": { + "normal_count": 244, + "normal_share": 0.8026315789473685, + "phishing_count": 4, + "phishing_share": 0.04819277108433735, + "sample_count": 248 + }, + "certain_phishing": { + "normal_count": 2, + "normal_share": 0.006578947368421052, + "phishing_count": 67, + "phishing_share": 0.8072289156626506, + "sample_count": 69 + }, + "uncertain": { + "normal_count": 58, + "normal_share": 0.19078947368421054, + "phishing_count": 12, + "phishing_share": 0.14457831325301204, + "sample_count": 70 + } + }, + "coverage_recall": 0.9518072289156626, + "edges": { + "normal_max": 0.16382862948494248, + "phishing_min": 0.8733913630982381 + }, + "missed_phishing_rate": 0.04819277108433735, + "normal_count": 304, + "phishing_count": 83, + "sample_count": 387, + "uncertain_share": 0.18087855297157623 + }, + "p95_latency_ms": 21.557700099999998, + "selection_split": "train_oof", + "synthetic_reference": { + "note": "합성 스트레스 셋은 참고용이며 판정에 사용하지 않는다", + "sample_count": 123 + } +} diff --git a/data_science/SMSModel/reports/error_analysis_v9.json b/data_science/SMSModel/reports/error_analysis_v9.json new file mode 100644 index 0000000..ac301ad --- /dev/null +++ b/data_science/SMSModel/reports/error_analysis_v9.json @@ -0,0 +1,775 @@ +{ + "artifact_threshold": 0.1638286294849425, + "splits": [ + { + "extreme_samples": { + "highest_scoring_normals": [ + { + "probability": 0.9613614571404062, + "text_fingerprint": "b6b057b5ff1c2ebf44d6b78e163265550a186a0060cadb0e88c96d9195050ccd", + "type": "정상공공기관알림" + }, + { + "probability": 0.9533123343273774, + "text_fingerprint": "ef74727f7a87cca63f06cdfaaad55e59a257585eba243376467b40cd3344f023", + "type": "기타정상" + }, + { + "probability": 0.9178726295107436, + "text_fingerprint": "af129827c3283f2218baef8752bf17dc48159f820b6793985bfcd7c83213f83b", + "type": "기타정상" + }, + { + "probability": 0.7393844577920894, + "text_fingerprint": "834771d380fd2b13133224da42ca21e35b3c22cdc67b1c4a1405e89160fe9330", + "type": "정상포인트소멸알림" + }, + { + "probability": 0.593457860115692, + "text_fingerprint": "aaf5ef89bfca869df0cd57fa6c0f63be41791a7a45c455bcd6734819ea23eca0", + "type": "정상포인트소멸알림" + }, + { + "probability": 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+ "missed_phishing": 10, + "target_fpr": 0.02, + "threshold": 0.7107659397108415 + }, + { + "max_recall": 0.9036144578313253, + "missed_phishing": 8, + "target_fpr": 0.05, + "threshold": 0.5445499908920339 + }, + { + "max_recall": 0.9156626506024096, + "missed_phishing": 7, + "target_fpr": 0.1, + "threshold": 0.43901535453186546 + }, + { + "max_recall": 0.927710843373494, + "missed_phishing": 6, + "target_fpr": 0.15, + "threshold": 0.252607775486969 + }, + { + "max_recall": 0.9518072289156626, + "missed_phishing": 4, + "target_fpr": 0.2, + "threshold": 0.17173481700215548 + } + ], + "phishing_count": 83, + "pr_auc": 0.9447738470653796, + "roc_auc": 0.9652821813570069, + "sample_count": 387, + "split": "real_holdout" + } + ], + "structural_feature_names": [ + "naive_bayes_score", + "logistic_regression_score", + "linear_svm_score", + "korean_encoder_score", + "has_url", + "has_short_url", + "has_phone", + "has_account", + "has_card", + "has_amount", + "has_web_tag", + "has_urgency", + "has_transfer_request", + "has_personal_info_request", + "has_link_action", + "is_long_text", + "has_opt_out", + "has_family_impersonation", + "has_chatroom_invite", + "has_job_offer_lure", + "has_investment_lure" + ] +} diff --git a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.json b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.json index d933ab5..7a2aa6d 100644 --- a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.json +++ b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.json @@ -1,6 +1,9 @@ { "error_samples": { "false_negatives": [ + { + "text_fingerprint": "057af781b64233fc26bb19129616eaefc14d5d78ea7b771e85c2daaf33cc0d11" + }, { "text_fingerprint": "102ecec1e3c66b3d3b2fee43a9b766dfbeea6807a2862be01d6e115b97ab909e" } @@ -12,12 +15,6 @@ { "text_fingerprint": "63fac6a7e948aa2daef3f106f0315a48e2361f6cd07047ce725966ccd42075cc" }, - { - "text_fingerprint": "52b243ae5b2c5906b0f4edf3661eb1c72f176d74dfc9b8c8858fdd93929dae64" - }, - { - "text_fingerprint": "3352ae1589dfba566e596eff5e5620e618998b4095267437e6b5e8b0bbc6b541" - }, { "text_fingerprint": "82bf23dc4e2ea1b0f2e88068e1bf3e139595263f342cc419c3cd0d006a357e22" }, @@ -36,15 +33,18 @@ { "text_fingerprint": "8853af0f6c41fd563514f394e1ec8009fca3837827c0b68f1e7b28ed940d51f0" }, + { + "text_fingerprint": "13ba85cb5b06529e2a3dae04f316a6ae2129c0dfec1f2092016c24ee94bbf180" + }, { "text_fingerprint": "10adc14bcef5e4426f96c073a9574376d71bf0594276cdd5d47422cd875f88cc" } ] }, "latency_stats": { - "mean": 10.564942925925926, - "p50": 9.387291, - "p95": 15.340725099999998, + "mean": 10.743186125925925, + "p50": 9.3995, + "p95": 15.8333921, "sample_count": 135 }, "normal_by_type": { @@ -69,14 +69,14 @@ "true_negative": 20 }, "정상공공기관알림": { - "false_positive": 2, - "false_positive_rate": 0.3333333333333333, + "false_positive": 1, + "false_positive_rate": 0.16666666666666666, "false_positive_rate_95_ci": { - "lower": 0.09676933255921683, - "upper": 0.7000116786584712 + "lower": 0.03005258587173032, + "upper": 0.563509436563646 }, "sample_count": 6, - "true_negative": 4 + "true_negative": 5 }, "정상광고프로모션": { "false_positive": 1, @@ -109,14 +109,14 @@ "true_negative": 9 }, "정상카드결제알림": { - "false_positive": 2, - "false_positive_rate": 0.25, + "false_positive": 1, + "false_positive_rate": 0.125, "false_positive_rate_95_ci": { - "lower": 0.07147768885802763, - "upper": 0.5907301208974108 + "lower": 0.02241690886329617, + "upper": 0.4708948057698615 }, "sample_count": 8, - "true_negative": 6 + "true_negative": 7 }, "정상택배배송안내": { "false_positive": 1, @@ -129,28 +129,28 @@ "true_negative": 12 }, "정상포인트소멸알림": { - "false_positive": 1, - "false_positive_rate": 0.14285714285714285, + "false_positive": 2, + "false_positive_rate": 0.2857142857142857, "false_positive_rate_95_ci": { - "lower": 0.02567895594897479, - "upper": 0.51313450331903 + "lower": 0.0822171657090155, + "upper": 0.6410709098517873 }, "sample_count": 7, - "true_negative": 6 + "true_negative": 5 } }, "overall_metrics": { "accuracy": 0.9111111111111111, "confusion_matrix": { - "false_negative": 1, - "false_positive": 11, - "true_negative": 81, - "true_positive": 42 + "false_negative": 2, + "false_positive": 10, + "true_negative": 82, + "true_positive": 41 }, - "f1_score": 0.875, - "f2_score": 0.9333333333333332, - "precision": 0.7924528301886793, - "recall": 0.9767441860465116 + "f1_score": 0.8723404255319148, + "f2_score": 0.9192825112107623, + "precision": 0.803921568627451, + "recall": 0.9534883720930233 }, "phishing_by_type": { "결제환불사칭": { @@ -274,14 +274,14 @@ "true_positive": 2 }, "채용부업사기": { - "false_negative": 0, - "recall": 1.0, + "false_negative": 1, + "recall": 0.0, "recall_95_ci": { - "lower": 0.20654329147389294, - "upper": 1.0 + "lower": 0.0, + "upper": 0.7934567085261071 }, "sample_count": 1, - "true_positive": 1 + "true_positive": 0 }, "택배배송사칭": { "false_negative": 0, @@ -316,29 +316,29 @@ "schema_version": 1, "shared_metrics": { "accuracy": 0.9111111111111111, - "f1": 0.875, - "f2": 0.9333333333333333, - "false_negative": 1, - "false_positive": 11, - "precision": 0.7924528301886793, - "recall": 0.9767441860465116, + "f1": 0.8723404255319149, + "f2": 0.9192825112107623, + "false_negative": 2, + "false_positive": 10, + "precision": 0.803921568627451, + "recall": 0.9534883720930233, "sample_count": 135, - "true_negative": 81, - "true_positive": 42 + "true_negative": 82, + "true_positive": 41 }, "split": "test", - "threshold": 0.12388533458915509, + "threshold": 0.1638286294849425, "unavailable_models": [], "unique_template_metrics": { "accuracy": 0.904, - "f1_score": 0.846153846153846, - "f2_score": 0.9166666666666666, - "false_negative": 1, - "false_positive": 11, - "precision": 0.75, - "recall": 0.9705882352941176, + "f1_score": 0.8421052631578947, + "f2_score": 0.8988764044943819, + "false_negative": 2, + "false_positive": 10, + "precision": 0.7619047619047619, + "recall": 0.9411764705882353, "sample_count": 125, - "true_negative": 80, - "true_positive": 33 + "true_negative": 81, + "true_positive": 32 } } diff --git a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md index f57d01e..136a1d5 100644 --- a/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md +++ b/data_science/SMSModel/reports/stacking_v9-experiment/test_evaluation.md @@ -6,14 +6,14 @@ Threshold selection: validation only; final metrics: test only. |---|---:| | Samples | 135 | | Accuracy | 0.911111 | -| Precision | 0.792453 | -| Recall | 0.976744 | -| F1 | 0.875000 | -| F2 | 0.933333 | -| TN | 81 | -| FP | 11 | -| FN | 1 | -| TP | 42 | -| Mean latency (ms) | 10.565 | -| P50 latency (ms) | 9.387 | -| P95 latency (ms) | 15.341 | +| Precision | 0.803922 | +| Recall | 0.953488 | +| F1 | 0.872340 | +| F2 | 0.919283 | +| TN | 82 | +| FP | 10 | +| FN | 2 | +| TP | 41 | +| Mean latency (ms) | 10.743 | +| P50 latency (ms) | 9.399 | +| P95 latency (ms) | 15.833 | diff --git a/data_science/SMSModel/train_sms.py b/data_science/SMSModel/train_sms.py index 8a39773..3c4fac7 100644 --- a/data_science/SMSModel/train_sms.py +++ b/data_science/SMSModel/train_sms.py @@ -136,6 +136,13 @@ def build_dataset_split_config() -> DatasetSplitConfig: test_size=0.15, random_state=42, candidate_count=500, + # selection_source는 켜지 않는다. pool 정상의 60%가 증강 데이터라 + # 선정용 split(validation)이 판정셋보다 쉬워지는 문제는 실재하지만 + # (#102 §5.3), original 우선으로 채워도 48% -> 58%에서 천장을 친다. + # 실패 유형의 실데이터가 7~18건뿐이라 유형 커버리지를 지키려면 + # 증강분을 섞을 수밖에 없기 때문이다. 지금 켜면 split이 바뀌어 + # 문서 5장의 판정 수치만 무효가 되고 얻는 것이 없다. + # 실수집 데이터가 확보되면 그때 켠다. ) diff --git a/docs/STANDALONE_ADOPTION_CRITERIA.md b/docs/STANDALONE_ADOPTION_CRITERIA.md index f749c39..1c75a96 100644 --- a/docs/STANDALONE_ADOPTION_CRITERIA.md +++ b/docs/STANDALONE_ADOPTION_CRITERIA.md @@ -170,52 +170,143 @@ python -m data_science.SMSModel.run_adoption_gate Recall과 오탐률이 상충해 둘 다 만족할 수 없을 때는 **오탐 상한을 지킨다.** 오탐이 많은 알림은 사용자가 꺼버려서 Recall이 0이 되기 때문이다. 임계값 선정 단계에서 이미 강제되며(`ThresholdInfeasibleError`), 상한을 지키는 운영점이 없으면 학습이 중단된다. -## 5. 현행 판정 — v7 +## 5. 현행 판정 — v9 -측정일 2026-08-21 · artifact `artifacts/stacking/v7` · 판정셋 391건(정상 308 + 피싱 83) +측정일 2026-08-23 · artifact `artifacts/stacking/v9-experiment` · 판정셋 387건(정상 304 + 피싱 83) -지연시간은 배포 환경과 같은 2 vCPU에서 측정했다(§2.6). `#100`에서 기준 두 개를 재산정했으므로, 과거 판정도 새 기준으로 다시 읽는다. +v9는 `#102`에서 base model을 파인튜닝 인코더로 교체하고 피싱 학습 데이터의 기관명 교체 아티팩트 22건을 제거한 결과다. 데이터셋이 바뀌어 판정셋이 391건에서 387건으로 줄었으므로, v7 이하와 비교할 때는 표본이 다르다는 점을 감안한다. -| 항목 | v5 | v6 | v7 | 기준 | v7 판정 | +| 항목 | v6 | v7 | **v9** | 기준 | v9 판정 | |---|---:|---:|---:|---:|---| -| 정상 표본 수 | 74 | 308 | 308 | ≥ 300 | 통과 | +| 정상 표본 수 | 308 | 308 | 304 | ≥ 300 | 통과 | | 피싱 표본 수 | 83 | 83 | 83 | ≥ 60 | 통과 | -| 피싱 포착률 | 0.8434 | 0.8554 | **0.8795** | ≥ 0.95 | **미달** | -| 자동 경고 오탐률 | 0.0000 | 0.0000 | 0.0000 | ≤ 0.01 | 통과 | -| '의심' 정상 비율 | 0.0676 | 0.1266 | 0.1201 | ≤ 0.133 | 통과 | -| P95 지연시간 | 15.66 ms | 11.65 ms | **54.89 ms** | ≤ 50 ms | **미달** | - -v5는 판정셋이 157건이던 시절의 기록이며 구조 특징이 14개라 현행 코드로 다시 측정할 수 없다. 표에 적힌 값은 당시 측정치 그대로이고, 기준만 새것으로 바꿔 읽었다. +| 피싱 포착률 | 0.8554 | 0.8795 | **0.9518** | ≥ 0.95 | **통과** | +| 자동 경고 오탐률 | 0.0000 | 0.0000 | 0.0066 | ≤ 0.01 | 통과 | +| '의심' 정상 비율 | 0.1266 | 0.1201 | **0.1908** | ≤ 0.133 | **미달** | +| P95 지연시간 | 11.65 ms | 54.89 ms | 측정 불가 | ≤ 50 ms | **판정 보류** | ### 종합: `FAIL` — 단독 운영으로 승격하지 않는다 -### 5.1 재산정으로 뒤집힌 항목 - -`'의심' 정상 비율`은 v6·v7이 기존 기준 0.10에서는 미달이었으나 재산정된 0.133에서는 통과한다. 값이 나아진 것이 아니라 **근거 없던 상한이 근거를 갖춘 값으로 바뀐 것**이다(§2.2). +### 5.1 피싱 포착률은 처음으로 통과했다 -`피싱 포착률`은 재산정 결과 0.95를 유지했다. 현행 하이브리드가 피싱을 놓치지 않으므로 완화할 근거가 없었다(§2.3). +v7까지 미달이던 포착률이 0.8795에서 0.9518로 올라 처음 기준을 넘겼다. `#102`의 인코더 파인튜닝과 데이터 정리가 함께 작용한 결과다. -### 5.2 인코더는 현재 인프라에서 배포할 수 없다 +`'의심' 정상 비율`은 v7의 0.1201에서 0.1908로 나빠진 것처럼 보이지만, 두 지표는 **경계를 사이에 둔 트레이드오프**다. v7은 경계를 낮게 잡아 의심 구간을 좁힌 대신 피싱을 놓쳤고, v9는 포착률을 확보한 대가로 의심 구간이 넓어졌다. 한쪽만 떼어 비교하면 오해가 생긴다. -v7은 `#98`에서 사전학습 인코더를 base model로 추가해 포착률을 0.8554에서 0.8795로 올렸다. 그러나 배포 환경 2 vCPU에서 P95가 54.89 ms로 예산을 넘는다. `max_length`를 128로 줄여도 인코더 단독 P95가 36.15 ms라 맞출 수 없다. +모델 자체의 실력은 임계값과 무관한 지표로 확인한다. -인코더가 없는 v6는 같은 조건에서 11.65 ms이며 **미달 항목이 `피싱 포착률` 하나뿐이다.** +| 지표 | 파인튜닝 전 | v9 | +|---|---:|---:| +| ROC-AUC | 0.9621 | 0.9653 | +| PR-AUC | 0.9250 | **0.9448** | -### 5.3 남은 원인은 학습셋 구성이다 +### 5.2 유형별 오탐률은 전 구간에서 개선됐다 -학습 pool의 피싱 데이터는 92%가 초기 `original`이며 한 번도 보강되지 않았다. `#83`·`#89`·`#93`의 개선은 모두 정상 문자와 판정셋에만 적용됐다. +artifact 임계값 기준 real_holdout 정상 304건. 임계값이 0.1502에서 0.1638로 함께 바뀌었으므로 절대값이 아니라 개선 폭으로 읽는다. -| 유형 | 학습% | 판정% | 차이 | +| 정상 유형 | 파인튜닝 전 | v9 | 변화 | |---|---:|---:|---:| -| 지인가족사칭 | 1.9 | 9.6 | +7.7 | -| 투자리딩방사기 | 1.6 | 4.8 | +3.2 | -| 채용부업사기 | 0.6 | 3.6 | +3.0 | -| 이벤트당첨사칭 | 24.9 | 22.9 | −2.0 | -| 경조사사칭 | 20.7 | 18.1 | −2.6 | +| 정상택배배송안내 | 78.6% | 32.1% | −46.4p | +| 정상금융알림 | 61.3% | 25.8% | −35.5p | +| 정상포인트소멸알림 | 52.2% | 17.4% | −34.8p | +| 정상광고프로모션 | 60.5% | 26.3% | −34.2p | +| 정상카드결제알림 | 63.0% | 29.6% | −33.3p | +| 정상공공기관알림 | 71.0% | 38.7% | −32.3p | +| 기타정상 | 45.5% | 15.2% | −30.3p | +| 정상인증알림 | 4.5% | 2.3% | −2.3p | +| 일상대화 | 2.0% | 6.1% | +4.1p | + +전체 오탐률 43.8% → **19.7%**. 사용자 관점에서는 정상 304건 중 무언가 표시되는 문자가 133건에서 60건으로 줄었다. + +### 5.3 미달 원인은 데이터다 — 임계값 정책도 모델도 아니다 + +`#102`에서 세 원인을 분리해 각각 실측으로 확인했다. + +**임계값 정책이 아니다.** 경계(`normal_max`/`phishing_min`)를 조합해 훑어도 두 기준을 동시에 만족하는 지점이 **하나도 없다**. `normal_max`를 0.16에서 0.20으로 올리면 '의심' 비율은 기준에 근접하지만 포착률이 0.9277로 먼저 무너진다. + +**모델이 아니다.** 다음 네 가지를 시도해 모두 실측으로 기각했다. + +| 시도 | 결과 | +|---|---| +| OOF fold 5 → 10 | 경계가 0.0597 → 0.0655로 거의 안 움직임 | +| 기관 대조 신호를 메타 피처로 | 기관 언급이 정상 19.5% vs 피싱 14.7%로 방향이 반대 | +| 정상 데이터 중복 제거 | 0.1908 → 0.3684으로 **악화** | +| learning rate warmup/decay | 0.1908 → 0.2928로 **악화** | + +정상 쪽 중복 제거가 악화된 이유는 기록해 둘 가치가 있다. 피싱에서는 같은 문자 9벌이 임계값 근처에 뭉쳐 recall 예산을 독식했지만, 정상에서는 `[삼성카드]/[현대카드]/[국민카드] 승인 45,000원...` 같은 변형이 **기관명이 무엇이든 이 템플릿은 정상**이라는 불변성을 가르치고 있었다. 같은 오염 패턴이라도 라벨에 따라 의미가 반대였다. + +**데이터다.** 오탐이 남은 유형은 실수집 데이터가 거의 없다. + +| 정상 유형 | 실데이터(`original`) | 합성·증강 | 실데이터 비율 | v9 오탐률 | +|---|---:|---:|---:|---:| +| 일상대화 | 136 | 3 | 98% | 6.1% | +| 기타정상 | 31 | 11 | 74% | 15.2% | +| 정상금융알림 | 23 | 18 | 56% | 25.8% | +| 정상공공기관알림 | 9 | 32 | 22% | 38.7% | +| 정상택배배송안내 | 18 | 77 | 19% | 32.1% | +| 정상카드결제알림 | 7 | 48 | 13% | 29.6% | +| 정상광고프로모션 | 14 | 112 | 11% | 26.3% | + +**실데이터 비율과 오탐률이 그대로 역상관이다.** 학습 pool 정상 439건 중 262건(60%)이 증강 데이터이며, 출처별 난이도를 OOF로 재면 증강분이 실데이터보다 훨씬 쉽다(정상 평균 확률 `synthetic_normal_v3` 0.099 / `real_collected_v4` 0.073 vs `original` 0.142, 판정셋 0.179). + +같은 이유로 **선정용 split도 만들 수 없다.** 임계값을 고르는 validation이 학습 pool에서 뽑히는데 그 60%가 쉬운 증강분이라, 7개 하이퍼파라미터 구성 전부에서 정상의 96.7~98.9%가 경계 아래로 내려가 변별이 안 됐다. 실제로 validation에서 가장 좋아 보이던 설정이 판정셋에서는 더 나빴다. `original`만으로 채우려 해도 실패 유형의 실데이터가 7~18건이라 유형 커버리지를 지킬 수 없다. + +### 5.4 인코더를 뺀 구성은 미달 항목이 하나 더 늘어난다 + +인프라 부담 없이 데이터 정리분만 반영하는 안(NB + LR + SVM 3개)을 측정했다. + +| 항목 | 파인튜닝 전 | 인코더 제외 | **v9(인코더)** | 기준 | +|---|---:|---:|---:|---:| +| '의심' 정상 비율 | 0.4342 | 0.3651 | **0.1908** | ≤ 0.133 | +| 자동 경고 오탐률 | 0.0033 | **0.0132** | 0.0066 | ≤ 0.01 | +| 피싱 포착률 | 0.9880 | 0.9518 | 0.9518 | ≥ 0.95 | +| 미달 항목 수 | 1 | **2** | 1 | | + +개선의 28%만 데이터 정리 몫이고 **72%가 인코더 몫**이다. 인코더를 빼면 `자동 경고 오탐률`까지 기준을 넘겨 미달 항목이 둘로 는다. + +### 5.5 인코더는 현재 인프라에 배포할 수 없다 — 판정 보류 + +v7에서 확인된 제약(§2.6)이 v9에도 그대로 적용된다. 파인튜닝은 forward 연산량을 바꾸지 않으므로 지연시간 특성이 v7과 같다. + +| 항목 | 추가 부담 | +|---|---:| +| `torch` + `transformers` | +617 MB (배포 이미지) | +| 추론 메모리(RSS) | 605 MB | +| 2 vCPU P95 (v7 실측) | 54.89 ms (예산 50 ms 초과) | + +배포 환경(`t3.small`)에 접근할 수 없어 v9의 P95를 §2.6 조건으로 측정하지 못했다. 개발 장비에서 `OMP_NUM_THREADS=2`로 재면 21.56 ms지만, 이는 애플 실리콘에서 스레드만 제한한 값이지 `t3.small` vCPU 성능이 아니다. v7도 같은 방식으로는 38.22 ms였으나 실제 배포 환경에서는 54.89 ms였다. + +**따라서 지연시간은 통과로 기록하지 않고 판정 보류로 남긴다.** 배포 환경에서 재측정하기 전까지 이 항목은 미확정이다. + +### 5.6 하이브리드에서의 실질 성과 + +단독 운영 승격과 별개로, 하이브리드에서 LLM에 넘기는 비율이 줄었다. + +| | LLM 호출 비율 | +|---|---:| +| 파인튜닝 전 | 40.3% | +| 인코더 제외 | 31.3% | +| **v9** | **18.1%** | + +판정셋 387건 기준 '확실한 정상' 172건 → 248건, '의심' 156건 → 70건이다. 이 수치는 채택 기준이 아니며 승격 근거로 쓰지 않는다. 다만 인코더 도입에 인프라 비용을 쓸지 판단할 때의 재료가 된다. + +### 5.7 다음 조치 — 실수집 정상 문자 확보 (§6-3) + +원인이 데이터이므로 수집 이슈로 분리한다. 필요한 것은 **정형 알림 형태의 실제 정상 문자**다. + +| 유형 | 현재 실데이터 | 목표 | 우선순위 | +|---|---:|---:|---| +| 정상카드결제알림 | 7 | 50+ | 높음 | +| 정상공공기관알림 | 9 | 50+ | 높음 | +| 정상광고프로모션 | 14 | 50+ | 높음 | +| 정상택배배송안내 | 18 | 50+ | 중간 | +| 정상금융알림 | 23 | 50+ | 중간 | + +일상대화는 실데이터 136건으로 오탐률이 6.1%다. 같은 수준의 실데이터가 쌓이면 나머지 유형도 같은 구간에 들어갈 것으로 본다. -**놓치는 세 유형이 정확히 격차가 가장 큰 세 유형이다.** 판정셋의 15.5%인데 학습에서는 4.1%다. +**합성 데이터로는 대체할 수 없다.** 이미 `synthetic_normal_v3` 211건이 있으나 OOF 실측에서 실데이터보다 뚜렷하게 쉬웠고(정상 평균 확률 0.099 vs `original` 0.142, 판정셋 0.179), 오탐이 남은 유형은 정확히 합성 비율이 높은 유형이다. 생성 모델은 전형적인 문장을 만드는데, 실제로 어려운 문자는 비전형적이기 때문에 어렵다. -`#96`의 구조 특징 추가와 `#98`의 인코더가 각각 1~2건씩만 움직인 이유가 여기 있다. 데이터 구성 문제를 모델링으로 풀려고 했다. 다음 조치는 세 유형의 실수집 피싱 문자 90건을 학습 pool에 보강하는 것이다. +수집분은 **학습 pool과 선정용 split 양쪽에 필요하다.** 판정셋은 지금대로 두어야 이전 판정과 비교가 유지된다. ## 6. 기준 미달 시 절차 From 53c7ef0e08c6f3bea4e665dbe5da68d5d7355d81 Mon Sep 17 00:00:00 2001 From: Gibeom Date: Sun, 23 Aug 2026 13:04:34 +0900 Subject: [PATCH 7/7] =?UTF-8?q?Fix:=20=EC=A0=95=EB=A6=AC=EB=90=9C=20?= =?UTF-8?q?=EB=8D=B0=EC=9D=B4=ED=84=B0=EC=85=8B=EC=9C=BC=EB=A1=9C=20?= =?UTF-8?q?=ED=94=84=EB=A1=9C=EB=8D=95=EC=85=98=20artifact=20=EC=9E=AC?= =?UTF-8?q?=EC=83=9D=EC=84=B1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit develop을 병합하니 #112가 추가한 가드가 곧바로 걸렸다. AssertionError: 프로덕션 artifact가 현재 데이터셋으로 학습되지 않았다. --deployable로 재학습해야 한다. 이 브랜치가 피싱 중복 22건을 제거해 데이터셋이 3584행에서 3562행으로 바뀌었는데, develop의 프로덕션 artifact는 정리 이전 데이터로 학습된 상태였다. 의도한 대로 동작한 것이라 --deployable로 재생성한다. ## 효과 (판정셋 real_holdout 387건) | 지표 | 정리 전 | 정리 후 | |---|---:|---:| | '의심' 정상 비율 | 0.6086 | 0.3651 | | 피싱 포착률 | 0.9759 | 0.9518 | | test_accuracy | 0.6449 | 0.8370 | 인코더를 뺀 배포 가능 구성이므로 채택 게이트는 여전히 FAIL이다 (문서 5.4절: 개선의 28%만 데이터 정리 몫이고 72%가 인코더 몫이며, 인코더를 빼면 자동 경고 오탐률까지 미달해 항목이 둘로 는다). 프로덕션은 인코더를 실행할 수 없으므로(torch 미포함) 이 구성이 현재 배포 가능한 최선이다. pytest tests/ 862개 통과. --- .../SMSModel/artifacts/stacking/metadata.json | 38 +-- .../SMSModel/artifacts/stacking/model.joblib | Bin 2624492 -> 2545696 bytes .../reports/adoption_gate_production.json | 58 ++-- .../stacking_production/test_evaluation.json | 275 ++++++------------ .../stacking_production/test_evaluation.md | 22 +- 5 files changed, 156 insertions(+), 237 deletions(-) diff --git a/data_science/SMSModel/artifacts/stacking/metadata.json b/data_science/SMSModel/artifacts/stacking/metadata.json index ea56576..edb4f33 100644 --- a/data_science/SMSModel/artifacts/stacking/metadata.json +++ b/data_science/SMSModel/artifacts/stacking/metadata.json @@ -1,11 +1,11 @@ { "artifact_version": "v9", - "created_at": "2026-08-23T03:45:42.286006+00:00", + "created_at": "2026-08-23T04:02:34.020134+00:00", "dataset": { - "dataset_fingerprint": "06c0756121240d61a0619d967fe128295500f21a6fbc6fcd2887052ace17ff3c", + "dataset_fingerprint": "500884cc4bd2ed9a45ce8b7dfbd16aff6c7672422bbe7959f5b190fcdc98b1b3", "holdout_rows": 510, - "total_csv_rows": 3584, - "training_pool_rows": 971 + "total_csv_rows": 3562, + "training_pool_rows": 949 }, "dataset_path": "Data/SMSData/phishing_total_dataset_reclassified.csv", "library_versions": { @@ -46,19 +46,19 @@ "has_investment_lure" ], "model_name": "stacking_phishing_classifier", - "oof_splits": 5, + "oof_splits": 10, "random_state": 42, - "threshold": 0.09572428195555654 + "threshold": 0.1501592596230264 }, - "model_configuration_sha256": "1f73f22f901a047e6abcff41d12dba5369da97ce803a8666dd17c3f4608dfe28", - "model_sha256": "bca83c5cba615a8634e52e844f5c1c413da8b37b5e50f68c5d332bb06aa0fefd", + "model_configuration_sha256": "2525b390b815ca60d58d73386f4ea28096c8e0cf857fc42f756217d3a24699b9", + "model_sha256": "fc5d2065c324df9783c4653b9f5d75e1774b651ab6beba15e1592948513112a5", "schema_version": 2, "split_manifest": "sms_split_v6.csv", - "split_manifest_sha256": "5899694abbae33038b6df21b4470f5bff846a62712ed20f734360454630a73f1", + "split_manifest_sha256": "214dd3385689f18907720b8f29c8710390672c7f3410fe3acd1af1ee74af17b2", "splits": { - "test": 138, - "train": 692, - "validation": 141 + "test": 135, + "train": 678, + "validation": 136 }, "training_policy": { "final_evaluation_split": "test", @@ -69,18 +69,18 @@ "training_split": "train" }, "validation": { - "f2": 0.7596685082872928, - "false_positive_rate": 0.6594360086767896, - "max_false_positive_rate": 0.6594360086767896, + "f2": 0.8596345514950167, + "false_positive_rate": 0.279826464208243, + "max_false_positive_rate": 0.279826464208243, "measurable_false_positive_rate": 0.0021691973969631237, - "recall": 0.9523809523809523, + "recall": 0.9539170506912442, "target_recall": 0.95, "target_recall_met": true }, "validation_reference": { - "false_positive_rate": 0.5760869565217391, + "false_positive_rate": 0.2717391304347826, "normal_count": 92, - "recall": 0.9795918367346939, - "sample_count": 141 + "recall": 0.9772727272727273, + "sample_count": 136 } } diff --git a/data_science/SMSModel/artifacts/stacking/model.joblib b/data_science/SMSModel/artifacts/stacking/model.joblib index ea7bd8d343e7d4c751616270a6645ddfd2e57bb7..3da4be038fb4e842dbdaa526d986749a67943906 100644 GIT binary patch literal 2545696 zcma&P37A#Iwf?QA_c^Ce+s21O>$yGdjj8nwXdvr$p(~CeBlg|F`z8RkeR7_j~T=bDvy2Z@+7;s#>*b 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0.019736842105263157, + "measured": 0.013157894736842105, "name": "alert_false_positive_rate", "outcome": "FAIL", "required": 0.01 }, { "detail": "'의심' 카드가 뜬 정상 비율", - "measured": 0.6085526315789473, + "measured": 0.3651315789473684, "name": "uncertain_normal_share", "outcome": "FAIL", "required": 0.133 }, { "detail": "단건 추론 P95 지연시간", - "measured": 7.3091661, + "measured": 7.1320794, "name": "p95_latency_ms", "outcome": "PASS", "required": 50.0 @@ -55,8 +55,8 @@ "min_phishing_samples": 60 }, "edges": { - "normal_max": 0.09572428195555653, - "phishing_min": 0.8380595369625472 + "normal_max": 0.15015925962302637, + "phishing_min": 0.7666808521109212 }, "judging_set": { "fingerprint": "55cd7d5b2687d233443079676109d177ce3e6f893ca5c32293e7c56922318c16", @@ -68,43 +68,43 @@ "omp_num_threads": "2" }, "measured": { - "alert_false_positive_rate": 0.019736842105263157, - "alert_recall": 0.6987951807228916, + "alert_false_positive_rate": 0.013157894736842105, + "alert_recall": 0.8313253012048193, "by_band": { "certain_normal": { - "normal_count": 113, - "normal_share": 0.3717105263157895, - "phishing_count": 2, - "phishing_share": 0.024096385542168676, - "sample_count": 115 + "normal_count": 189, + "normal_share": 0.6217105263157895, + "phishing_count": 4, + "phishing_share": 0.04819277108433735, + "sample_count": 193 }, "certain_phishing": { - "normal_count": 6, - "normal_share": 0.019736842105263157, - "phishing_count": 58, - "phishing_share": 0.6987951807228916, - "sample_count": 64 + "normal_count": 4, + "normal_share": 0.013157894736842105, + "phishing_count": 69, + "phishing_share": 0.8313253012048193, + "sample_count": 73 }, "uncertain": { - "normal_count": 185, - "normal_share": 0.6085526315789473, - "phishing_count": 23, - "phishing_share": 0.27710843373493976, - "sample_count": 208 + "normal_count": 111, + "normal_share": 0.3651315789473684, + "phishing_count": 10, + "phishing_share": 0.12048192771084337, + "sample_count": 121 } }, - "coverage_recall": 0.9759036144578314, + "coverage_recall": 0.9518072289156626, "edges": { - "normal_max": 0.09572428195555653, - "phishing_min": 0.8380595369625472 + "normal_max": 0.15015925962302637, + "phishing_min": 0.7666808521109212 }, - "missed_phishing_rate": 0.024096385542168676, + "missed_phishing_rate": 0.04819277108433735, "normal_count": 304, "phishing_count": 83, "sample_count": 387, - "uncertain_share": 0.537467700258398 + "uncertain_share": 0.31266149870801035 }, - "p95_latency_ms": 7.3091661, + "p95_latency_ms": 7.1320794, "selection_split": "train_oof", "synthetic_reference": { "note": "합성 스트레스 셋은 참고용이며 판정에 사용하지 않는다", diff --git a/data_science/SMSModel/reports/stacking_production/test_evaluation.json b/data_science/SMSModel/reports/stacking_production/test_evaluation.json index 767d8cd..246b883 100644 --- a/data_science/SMSModel/reports/stacking_production/test_evaluation.json +++ b/data_science/SMSModel/reports/stacking_production/test_evaluation.json @@ -3,230 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{ "기타정상": { - "false_positive": 4, - "false_positive_rate": 0.6666666666666666, + "false_positive": 2, + "false_positive_rate": 0.3333333333333333, "false_positive_rate_95_ci": { - "lower": 0.29998832134152864, - "upper": 0.9032306674407831 + "lower": 0.09676933255921683, + "upper": 0.7000116786584712 }, "sample_count": 6, - "true_negative": 2 + "true_negative": 4 }, "일상대화": { - "false_positive": 10, - "false_positive_rate": 0.47619047619047616, + "false_positive": 2, + "false_positive_rate": 0.09523809523809523, "false_positive_rate_95_ci": { - "lower": 0.28343712296762946, - "upper": 0.6763078209973324 + "lower": 0.026518208796947246, + "upper": 0.2891458386074067 }, "sample_count": 21, - "true_negative": 11 + "true_negative": 19 }, "정상공공기관알림": { - "false_positive": 3, - "false_positive_rate": 0.5, + "false_positive": 1, + "false_positive_rate": 0.16666666666666666, "false_positive_rate_95_ci": { - "lower": 0.18761280689940868, - "upper": 0.8123871931005913 + "lower": 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"lower": 0.35419734749908977, - "upper": 0.879418401317257 + "lower": 0.06322376231222723, + "upper": 0.5474166563271946 }, "sample_count": 9, - "true_negative": 3 + "true_negative": 7 }, "정상카드결제알림": { - "false_positive": 8, - "false_positive_rate": 1.0, + "false_positive": 3, + "false_positive_rate": 0.375, "false_positive_rate_95_ci": { - "lower": 0.6755843804891231, - "upper": 1.0 + "lower": 0.13684175946152105, + "upper": 0.6942621454161981 }, "sample_count": 8, - "true_negative": 0 + "true_negative": 5 }, "정상택배배송안내": { "false_positive": 4, @@ -250,16 +169,16 @@ } }, "overall_metrics": { - "accuracy": 0.644927536231884, + "accuracy": 0.837037037037037, "confusion_matrix": { "false_negative": 0, - "false_positive": 49, - "true_negative": 43, - "true_positive": 46 + "false_positive": 22, + "true_negative": 70, + "true_positive": 43 }, - "f1_score": 0.6524822695035462, - "f2_score": 0.8243727598566308, - "precision": 0.4842105263157895, + "f1_score": 0.7962962962962964, + "f2_score": 0.9071729957805906, + "precision": 0.6615384615384615, "recall": 1.0 }, "phishing_by_type": { @@ -277,11 +196,11 @@ "false_negative": 0, "recall": 1.0, "recall_95_ci": { - "lower": 0.6755843804891231, + "lower": 0.7008472464490407, "upper": 1.0 }, - "sample_count": 8, - "true_positive": 8 + "sample_count": 9, + "true_positive": 9 }, "계정정지본인인증유도": { "false_negative": 0, @@ -297,11 +216,11 @@ "false_negative": 0, "recall": 1.0, "recall_95_ci": { - "lower": 0.7008472464490407, + "lower": 0.5655085052479191, "upper": 1.0 }, - "sample_count": 9, - "true_positive": 9 + "sample_count": 5, + "true_positive": 5 }, "기타피싱": { "false_negative": 0, @@ -357,11 +276,11 @@ "false_negative": 0, "recall": 1.0, "recall_95_ci": { - "lower": 0.5655085052479191, + "lower": 0.5100999795960008, "upper": 1.0 }, - "sample_count": 5, - "true_positive": 5 + "sample_count": 4, + "true_positive": 4 }, "중고거래사기": { "false_negative": 0, @@ -397,11 +316,11 @@ "false_negative": 0, "recall": 1.0, "recall_95_ci": { - "lower": 0.5100999795960008, + "lower": 0.5655085052479191, "upper": 1.0 }, - "sample_count": 4, - "true_positive": 4 + "sample_count": 5, + "true_positive": 5 }, "투자리딩방사기": { "false_negative": 0, @@ -420,35 +339,35 @@ "exception": 0, "missing_result": 0 }, - "successful_samples": 138, - "total_samples": 138 + "successful_samples": 135, + "total_samples": 135 }, "schema_version": 1, "shared_metrics": { - "accuracy": 0.644927536231884, - "f1": 0.6524822695035462, - "f2": 0.8243727598566308, + "accuracy": 0.837037037037037, + "f1": 0.7962962962962963, + "f2": 0.9071729957805907, "false_negative": 0, - "false_positive": 49, - "precision": 0.4842105263157895, + "false_positive": 22, + "precision": 0.6615384615384615, "recall": 1.0, - "sample_count": 138, - "true_negative": 43, - "true_positive": 46 + "sample_count": 135, + "true_negative": 70, + "true_positive": 43 }, "split": "test", - "threshold": 0.09572428195555654, + "threshold": 0.1501592596230264, "unavailable_models": [], "unique_template_metrics": { - "accuracy": 0.6097560975609756, - "f1_score": 0.5714285714285715, - "f2_score": 0.7692307692307692, + "accuracy": 0.824, + "f1_score": 0.7555555555555554, + "f2_score": 0.8854166666666666, "false_negative": 0, - "false_positive": 48, - "precision": 0.4, + "false_positive": 22, + "precision": 0.6071428571428571, "recall": 1.0, - "sample_count": 123, - "true_negative": 43, - "true_positive": 32 + "sample_count": 125, + "true_negative": 69, + "true_positive": 34 } } diff --git a/data_science/SMSModel/reports/stacking_production/test_evaluation.md b/data_science/SMSModel/reports/stacking_production/test_evaluation.md index 68d6fed..2f3abfb 100644 --- a/data_science/SMSModel/reports/stacking_production/test_evaluation.md +++ b/data_science/SMSModel/reports/stacking_production/test_evaluation.md @@ -4,16 +4,16 @@ Threshold selection: validation only; final metrics: test only. | Metric | Value | |---|---:| -| Samples | 138 | -| Accuracy | 0.644928 | -| Precision | 0.484211 | +| Samples | 135 | +| Accuracy | 0.837037 | +| Precision | 0.661538 | | Recall | 1.000000 | -| F1 | 0.652482 | -| F2 | 0.824373 | -| TN | 43 | -| FP | 49 | +| F1 | 0.796296 | +| F2 | 0.907173 | +| TN | 70 | +| FP | 22 | | FN | 0 | -| TP | 46 | -| Mean latency (ms) | 3.919 | -| P50 latency (ms) | 3.567 | -| P95 latency (ms) | 5.498 | +| TP | 43 | +| Mean latency (ms) | 4.101 | +| P50 latency (ms) | 3.715 | +| P95 latency (ms) | 5.567 |